795 lines
27 KiB
Python
Executable File
795 lines
27 KiB
Python
Executable File
# coding=utf-8
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# Copyright 2022 HuggingFace Inc.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import inspect
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import tempfile
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import unittest
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import numpy as np
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import torch
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from diffusers import (
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BDDMPipeline,
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DDIMPipeline,
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DDIMScheduler,
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DDPMPipeline,
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DDPMScheduler,
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GlidePipeline,
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GlideSuperResUNetModel,
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GlideTextToImageUNetModel,
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GradTTSPipeline,
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GradTTSScheduler,
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LatentDiffusionPipeline,
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NCSNpp,
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PNDMPipeline,
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PNDMScheduler,
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ScoreSdeVePipeline,
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ScoreSdeVeScheduler,
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ScoreSdeVpPipeline,
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ScoreSdeVpScheduler,
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UNetGradTTSModel,
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UNetLDMModel,
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UNetModel,
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)
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from diffusers.configuration_utils import ConfigMixin
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from diffusers.pipeline_utils import DiffusionPipeline
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from diffusers.pipelines.pipeline_bddm import DiffWave
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from diffusers.testing_utils import floats_tensor, slow, torch_device
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torch.backends.cuda.matmul.allow_tf32 = False
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class ConfigTester(unittest.TestCase):
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def test_load_not_from_mixin(self):
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with self.assertRaises(ValueError):
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ConfigMixin.from_config("dummy_path")
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def test_save_load(self):
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class SampleObject(ConfigMixin):
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config_name = "config.json"
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def __init__(
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self,
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a=2,
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b=5,
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c=(2, 5),
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d="for diffusion",
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e=[1, 3],
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):
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self.register_to_config(a=a, b=b, c=c, d=d, e=e)
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obj = SampleObject()
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config = obj.config
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assert config["a"] == 2
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assert config["b"] == 5
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assert config["c"] == (2, 5)
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assert config["d"] == "for diffusion"
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assert config["e"] == [1, 3]
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with tempfile.TemporaryDirectory() as tmpdirname:
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obj.save_config(tmpdirname)
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new_obj = SampleObject.from_config(tmpdirname)
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new_config = new_obj.config
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# unfreeze configs
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config = dict(config)
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new_config = dict(new_config)
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assert config.pop("c") == (2, 5) # instantiated as tuple
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assert new_config.pop("c") == [2, 5] # saved & loaded as list because of json
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assert config == new_config
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class ModelTesterMixin:
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def test_from_pretrained_save_pretrained(self):
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init_dict, inputs_dict = self.prepare_init_args_and_inputs_for_common()
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model = self.model_class(**init_dict)
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model.to(torch_device)
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model.eval()
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with tempfile.TemporaryDirectory() as tmpdirname:
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model.save_pretrained(tmpdirname)
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new_model = self.model_class.from_pretrained(tmpdirname)
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new_model.to(torch_device)
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with torch.no_grad():
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image = model(**inputs_dict)
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new_image = new_model(**inputs_dict)
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max_diff = (image - new_image).abs().sum().item()
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self.assertLessEqual(max_diff, 5e-5, "Models give different forward passes")
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def test_determinism(self):
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init_dict, inputs_dict = self.prepare_init_args_and_inputs_for_common()
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model = self.model_class(**init_dict)
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model.to(torch_device)
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model.eval()
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with torch.no_grad():
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first = model(**inputs_dict)
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second = model(**inputs_dict)
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out_1 = first.cpu().numpy()
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out_2 = second.cpu().numpy()
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out_1 = out_1[~np.isnan(out_1)]
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out_2 = out_2[~np.isnan(out_2)]
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max_diff = np.amax(np.abs(out_1 - out_2))
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self.assertLessEqual(max_diff, 1e-5)
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def test_output(self):
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init_dict, inputs_dict = self.prepare_init_args_and_inputs_for_common()
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model = self.model_class(**init_dict)
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model.to(torch_device)
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model.eval()
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with torch.no_grad():
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output = model(**inputs_dict)
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self.assertIsNotNone(output)
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expected_shape = inputs_dict["x"].shape
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self.assertEqual(output.shape, expected_shape, "Input and output shapes do not match")
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def test_forward_signature(self):
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init_dict, _ = self.prepare_init_args_and_inputs_for_common()
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model = self.model_class(**init_dict)
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signature = inspect.signature(model.forward)
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# signature.parameters is an OrderedDict => so arg_names order is deterministic
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arg_names = [*signature.parameters.keys()]
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expected_arg_names = ["x", "timesteps"]
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self.assertListEqual(arg_names[:2], expected_arg_names)
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def test_model_from_config(self):
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init_dict, inputs_dict = self.prepare_init_args_and_inputs_for_common()
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model = self.model_class(**init_dict)
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model.to(torch_device)
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model.eval()
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# test if the model can be loaded from the config
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# and has all the expected shape
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with tempfile.TemporaryDirectory() as tmpdirname:
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model.save_config(tmpdirname)
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new_model = self.model_class.from_config(tmpdirname)
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new_model.to(torch_device)
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new_model.eval()
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# check if all paramters shape are the same
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for param_name in model.state_dict().keys():
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param_1 = model.state_dict()[param_name]
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param_2 = new_model.state_dict()[param_name]
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self.assertEqual(param_1.shape, param_2.shape)
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with torch.no_grad():
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output_1 = model(**inputs_dict)
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output_2 = new_model(**inputs_dict)
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self.assertEqual(output_1.shape, output_2.shape)
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def test_training(self):
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init_dict, inputs_dict = self.prepare_init_args_and_inputs_for_common()
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model = self.model_class(**init_dict)
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model.to(torch_device)
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model.train()
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output = model(**inputs_dict)
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noise = torch.randn((inputs_dict["x"].shape[0],) + self.get_output_shape).to(torch_device)
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loss = torch.nn.functional.mse_loss(output, noise)
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loss.backward()
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class UnetModelTests(ModelTesterMixin, unittest.TestCase):
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model_class = UNetModel
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@property
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def dummy_input(self):
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batch_size = 4
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num_channels = 3
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sizes = (32, 32)
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noise = floats_tensor((batch_size, num_channels) + sizes).to(torch_device)
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time_step = torch.tensor([10]).to(torch_device)
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return {"x": noise, "timesteps": time_step}
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@property
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def get_input_shape(self):
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return (3, 32, 32)
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@property
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def get_output_shape(self):
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return (3, 32, 32)
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def prepare_init_args_and_inputs_for_common(self):
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init_dict = {
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"ch": 32,
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"ch_mult": (1, 2),
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"num_res_blocks": 2,
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"attn_resolutions": (16,),
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"resolution": 32,
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}
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inputs_dict = self.dummy_input
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return init_dict, inputs_dict
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def test_from_pretrained_hub(self):
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model, loading_info = UNetModel.from_pretrained("fusing/ddpm_dummy", output_loading_info=True)
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self.assertIsNotNone(model)
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self.assertEqual(len(loading_info["missing_keys"]), 0)
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model.to(torch_device)
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image = model(**self.dummy_input)
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assert image is not None, "Make sure output is not None"
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def test_output_pretrained(self):
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model = UNetModel.from_pretrained("fusing/ddpm_dummy")
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model.eval()
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torch.manual_seed(0)
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if torch.cuda.is_available():
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torch.cuda.manual_seed_all(0)
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noise = torch.randn(1, model.config.in_channels, model.config.resolution, model.config.resolution)
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time_step = torch.tensor([10])
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with torch.no_grad():
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output = model(noise, time_step)
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output_slice = output[0, -1, -3:, -3:].flatten()
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# fmt: off
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expected_output_slice = torch.tensor([0.2891, -0.1899, 0.2595, -0.6214, 0.0968, -0.2622, 0.4688, 0.1311, 0.0053])
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# fmt: on
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self.assertTrue(torch.allclose(output_slice, expected_output_slice, atol=1e-3))
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class GlideSuperResUNetTests(ModelTesterMixin, unittest.TestCase):
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model_class = GlideSuperResUNetModel
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@property
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def dummy_input(self):
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batch_size = 4
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num_channels = 6
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sizes = (32, 32)
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low_res_size = (4, 4)
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noise = torch.randn((batch_size, num_channels // 2) + sizes).to(torch_device)
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low_res = torch.randn((batch_size, 3) + low_res_size).to(torch_device)
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time_step = torch.tensor([10] * noise.shape[0], device=torch_device)
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return {"x": noise, "timesteps": time_step, "low_res": low_res}
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@property
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def get_input_shape(self):
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return (3, 32, 32)
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@property
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def get_output_shape(self):
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return (6, 32, 32)
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def prepare_init_args_and_inputs_for_common(self):
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init_dict = {
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"attention_resolutions": (2,),
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"channel_mult": (1, 2),
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"in_channels": 6,
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"out_channels": 6,
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"model_channels": 32,
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"num_head_channels": 8,
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"num_heads_upsample": 1,
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"num_res_blocks": 2,
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"resblock_updown": True,
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"resolution": 32,
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"use_scale_shift_norm": True,
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}
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inputs_dict = self.dummy_input
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return init_dict, inputs_dict
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def test_output(self):
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init_dict, inputs_dict = self.prepare_init_args_and_inputs_for_common()
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model = self.model_class(**init_dict)
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model.to(torch_device)
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model.eval()
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with torch.no_grad():
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output = model(**inputs_dict)
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output, _ = torch.split(output, 3, dim=1)
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self.assertIsNotNone(output)
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expected_shape = inputs_dict["x"].shape
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self.assertEqual(output.shape, expected_shape, "Input and output shapes do not match")
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def test_from_pretrained_hub(self):
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model, loading_info = GlideSuperResUNetModel.from_pretrained(
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"fusing/glide-super-res-dummy", output_loading_info=True
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)
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self.assertIsNotNone(model)
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self.assertEqual(len(loading_info["missing_keys"]), 0)
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model.to(torch_device)
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image = model(**self.dummy_input)
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assert image is not None, "Make sure output is not None"
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def test_output_pretrained(self):
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model = GlideSuperResUNetModel.from_pretrained("fusing/glide-super-res-dummy")
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torch.manual_seed(0)
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if torch.cuda.is_available():
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torch.cuda.manual_seed_all(0)
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noise = torch.randn(1, 3, 64, 64)
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low_res = torch.randn(1, 3, 4, 4)
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time_step = torch.tensor([42] * noise.shape[0])
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with torch.no_grad():
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output = model(noise, time_step, low_res)
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output, _ = torch.split(output, 3, dim=1)
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output_slice = output[0, -1, -3:, -3:].flatten()
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# fmt: off
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expected_output_slice = torch.tensor([-22.8782, -23.2652, -15.3966, -22.8034, -23.3159, -15.5640, -15.3970, -15.4614, - 10.4370])
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# fmt: on
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self.assertTrue(torch.allclose(output_slice, expected_output_slice, atol=1e-3))
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class GlideTextToImageUNetModelTests(ModelTesterMixin, unittest.TestCase):
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model_class = GlideTextToImageUNetModel
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@property
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def dummy_input(self):
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batch_size = 4
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num_channels = 3
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sizes = (32, 32)
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transformer_dim = 32
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seq_len = 16
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noise = torch.randn((batch_size, num_channels) + sizes).to(torch_device)
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emb = torch.randn((batch_size, seq_len, transformer_dim)).to(torch_device)
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time_step = torch.tensor([10] * noise.shape[0], device=torch_device)
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return {"x": noise, "timesteps": time_step, "transformer_out": emb}
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@property
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def get_input_shape(self):
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return (3, 32, 32)
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@property
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def get_output_shape(self):
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return (6, 32, 32)
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def prepare_init_args_and_inputs_for_common(self):
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init_dict = {
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"attention_resolutions": (2,),
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"channel_mult": (1, 2),
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"in_channels": 3,
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"out_channels": 6,
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"model_channels": 32,
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"num_head_channels": 8,
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"num_heads_upsample": 1,
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"num_res_blocks": 2,
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"resblock_updown": True,
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"resolution": 32,
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"use_scale_shift_norm": True,
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"transformer_dim": 32,
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}
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inputs_dict = self.dummy_input
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return init_dict, inputs_dict
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def test_output(self):
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init_dict, inputs_dict = self.prepare_init_args_and_inputs_for_common()
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model = self.model_class(**init_dict)
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model.to(torch_device)
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model.eval()
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with torch.no_grad():
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output = model(**inputs_dict)
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output, _ = torch.split(output, 3, dim=1)
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self.assertIsNotNone(output)
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expected_shape = inputs_dict["x"].shape
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self.assertEqual(output.shape, expected_shape, "Input and output shapes do not match")
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def test_from_pretrained_hub(self):
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model, loading_info = GlideTextToImageUNetModel.from_pretrained(
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"fusing/unet-glide-text2im-dummy", output_loading_info=True
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)
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self.assertIsNotNone(model)
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self.assertEqual(len(loading_info["missing_keys"]), 0)
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model.to(torch_device)
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image = model(**self.dummy_input)
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assert image is not None, "Make sure output is not None"
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def test_output_pretrained(self):
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model = GlideTextToImageUNetModel.from_pretrained("fusing/unet-glide-text2im-dummy")
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torch.manual_seed(0)
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if torch.cuda.is_available():
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torch.cuda.manual_seed_all(0)
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noise = torch.randn((1, model.config.in_channels, model.config.resolution, model.config.resolution)).to(
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torch_device
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)
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emb = torch.randn((1, 16, model.config.transformer_dim)).to(torch_device)
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time_step = torch.tensor([10] * noise.shape[0], device=torch_device)
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model.to(torch_device)
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with torch.no_grad():
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output = model(noise, time_step, emb)
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output, _ = torch.split(output, 3, dim=1)
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output_slice = output[0, -1, -3:, -3:].cpu().flatten()
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# fmt: off
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expected_output_slice = torch.tensor([2.7766, -10.3558, -14.9149, -0.9376, -14.9175, -17.7679, -5.5565, -12.9521, -12.9845])
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# fmt: on
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self.assertTrue(torch.allclose(output_slice, expected_output_slice, atol=1e-3))
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class UNetLDMModelTests(ModelTesterMixin, unittest.TestCase):
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model_class = UNetLDMModel
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@property
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def dummy_input(self):
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batch_size = 4
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num_channels = 4
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sizes = (32, 32)
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noise = floats_tensor((batch_size, num_channels) + sizes).to(torch_device)
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time_step = torch.tensor([10]).to(torch_device)
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return {"x": noise, "timesteps": time_step}
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@property
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def get_input_shape(self):
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return (4, 32, 32)
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@property
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def get_output_shape(self):
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return (4, 32, 32)
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def prepare_init_args_and_inputs_for_common(self):
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init_dict = {
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"image_size": 32,
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"in_channels": 4,
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"out_channels": 4,
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"model_channels": 32,
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"num_res_blocks": 2,
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"attention_resolutions": (16,),
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"channel_mult": (1, 2),
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"num_heads": 2,
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"conv_resample": True,
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}
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inputs_dict = self.dummy_input
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return init_dict, inputs_dict
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def test_from_pretrained_hub(self):
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model, loading_info = UNetLDMModel.from_pretrained("fusing/unet-ldm-dummy", output_loading_info=True)
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self.assertIsNotNone(model)
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self.assertEqual(len(loading_info["missing_keys"]), 0)
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model.to(torch_device)
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image = model(**self.dummy_input)
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assert image is not None, "Make sure output is not None"
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def test_output_pretrained(self):
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model = UNetLDMModel.from_pretrained("fusing/unet-ldm-dummy")
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model.eval()
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torch.manual_seed(0)
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if torch.cuda.is_available():
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|
torch.cuda.manual_seed_all(0)
|
|
|
|
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()
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|
# 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
|
|
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|
self.assertTrue(torch.allclose(output_slice, expected_output_slice, atol=1e-3))
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|
|
|
|
|
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
|
|
@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):
|
|
torch.manual_seed(0)
|
|
|
|
model = NCSNpp.from_pretrained("fusing/ffhq_ncsnpp")
|
|
scheduler = ScoreSdeVeScheduler.from_config("fusing/ffhq_ncsnpp")
|
|
|
|
sde_ve = ScoreSdeVePipeline(model=model, scheduler=scheduler)
|
|
|
|
image = sde_ve(num_inference_steps=2)
|
|
|
|
expected_image_sum = 3382810112.0
|
|
expected_image_mean = 1075.366455078125
|
|
|
|
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
|
|
|
|
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)
|