739 lines
29 KiB
Python
Executable File
739 lines
29 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 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 DDIMScheduler, DDPMScheduler, PNDMScheduler, ScoreSdeVeScheduler
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torch.backends.cuda.matmul.allow_tf32 = False
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class SchedulerCommonTest(unittest.TestCase):
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scheduler_classes = ()
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forward_default_kwargs = ()
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@property
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def dummy_sample(self):
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batch_size = 4
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num_channels = 3
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height = 8
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width = 8
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sample = torch.rand((batch_size, num_channels, height, width))
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return sample
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@property
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def dummy_sample_deter(self):
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batch_size = 4
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num_channels = 3
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height = 8
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width = 8
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num_elems = batch_size * num_channels * height * width
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sample = torch.arange(num_elems)
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sample = sample.reshape(num_channels, height, width, batch_size)
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sample = sample / num_elems
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sample = sample.permute(3, 0, 1, 2)
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return sample
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def get_scheduler_config(self):
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raise NotImplementedError
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def dummy_model(self):
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def model(sample, t, *args):
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return sample * t / (t + 1)
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return model
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def check_over_configs(self, time_step=0, **config):
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kwargs = dict(self.forward_default_kwargs)
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num_inference_steps = kwargs.pop("num_inference_steps", None)
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for scheduler_class in self.scheduler_classes:
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sample = self.dummy_sample
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residual = 0.1 * sample
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scheduler_config = self.get_scheduler_config(**config)
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scheduler = scheduler_class(**scheduler_config)
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with tempfile.TemporaryDirectory() as tmpdirname:
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scheduler.save_config(tmpdirname)
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new_scheduler = scheduler_class.from_config(tmpdirname)
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if num_inference_steps is not None and hasattr(scheduler, "set_timesteps"):
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scheduler.set_timesteps(num_inference_steps)
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new_scheduler.set_timesteps(num_inference_steps)
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elif num_inference_steps is not None and not hasattr(scheduler, "set_timesteps"):
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kwargs["num_inference_steps"] = num_inference_steps
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output = scheduler.step(residual, time_step, sample, **kwargs)["prev_sample"]
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new_output = new_scheduler.step(residual, time_step, sample, **kwargs)["prev_sample"]
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assert torch.sum(torch.abs(output - new_output)) < 1e-5, "Scheduler outputs are not identical"
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def check_over_forward(self, time_step=0, **forward_kwargs):
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kwargs = dict(self.forward_default_kwargs)
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kwargs.update(forward_kwargs)
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num_inference_steps = kwargs.pop("num_inference_steps", None)
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for scheduler_class in self.scheduler_classes:
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sample = self.dummy_sample
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residual = 0.1 * sample
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scheduler_config = self.get_scheduler_config()
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scheduler = scheduler_class(**scheduler_config)
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with tempfile.TemporaryDirectory() as tmpdirname:
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scheduler.save_config(tmpdirname)
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new_scheduler = scheduler_class.from_config(tmpdirname)
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if num_inference_steps is not None and hasattr(scheduler, "set_timesteps"):
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scheduler.set_timesteps(num_inference_steps)
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new_scheduler.set_timesteps(num_inference_steps)
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elif num_inference_steps is not None and not hasattr(scheduler, "set_timesteps"):
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kwargs["num_inference_steps"] = num_inference_steps
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torch.manual_seed(0)
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output = scheduler.step(residual, time_step, sample, **kwargs)["prev_sample"]
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torch.manual_seed(0)
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new_output = new_scheduler.step(residual, time_step, sample, **kwargs)["prev_sample"]
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assert torch.sum(torch.abs(output - new_output)) < 1e-5, "Scheduler outputs are not identical"
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def test_from_pretrained_save_pretrained(self):
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kwargs = dict(self.forward_default_kwargs)
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num_inference_steps = kwargs.pop("num_inference_steps", None)
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for scheduler_class in self.scheduler_classes:
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sample = self.dummy_sample
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residual = 0.1 * sample
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scheduler_config = self.get_scheduler_config()
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scheduler = scheduler_class(**scheduler_config)
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with tempfile.TemporaryDirectory() as tmpdirname:
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scheduler.save_config(tmpdirname)
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new_scheduler = scheduler_class.from_config(tmpdirname)
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if num_inference_steps is not None and hasattr(scheduler, "set_timesteps"):
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scheduler.set_timesteps(num_inference_steps)
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new_scheduler.set_timesteps(num_inference_steps)
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elif num_inference_steps is not None and not hasattr(scheduler, "set_timesteps"):
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kwargs["num_inference_steps"] = num_inference_steps
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torch.manual_seed(0)
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output = scheduler.step(residual, 1, sample, **kwargs)["prev_sample"]
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torch.manual_seed(0)
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new_output = new_scheduler.step(residual, 1, sample, **kwargs)["prev_sample"]
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assert torch.sum(torch.abs(output - new_output)) < 1e-5, "Scheduler outputs are not identical"
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def test_step_shape(self):
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kwargs = dict(self.forward_default_kwargs)
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num_inference_steps = kwargs.pop("num_inference_steps", None)
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for scheduler_class in self.scheduler_classes:
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scheduler_config = self.get_scheduler_config()
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scheduler = scheduler_class(**scheduler_config)
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sample = self.dummy_sample
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residual = 0.1 * sample
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if num_inference_steps is not None and hasattr(scheduler, "set_timesteps"):
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scheduler.set_timesteps(num_inference_steps)
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elif num_inference_steps is not None and not hasattr(scheduler, "set_timesteps"):
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kwargs["num_inference_steps"] = num_inference_steps
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output_0 = scheduler.step(residual, 0, sample, **kwargs)["prev_sample"]
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output_1 = scheduler.step(residual, 1, sample, **kwargs)["prev_sample"]
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self.assertEqual(output_0.shape, sample.shape)
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self.assertEqual(output_0.shape, output_1.shape)
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def test_pytorch_equal_numpy(self):
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kwargs = dict(self.forward_default_kwargs)
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num_inference_steps = kwargs.pop("num_inference_steps", None)
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for scheduler_class in self.scheduler_classes:
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sample_pt = self.dummy_sample
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residual_pt = 0.1 * sample_pt
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sample = sample_pt.numpy()
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residual = 0.1 * sample
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scheduler_config = self.get_scheduler_config()
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scheduler = scheduler_class(tensor_format="np", **scheduler_config)
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scheduler_pt = scheduler_class(tensor_format="pt", **scheduler_config)
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if num_inference_steps is not None and hasattr(scheduler, "set_timesteps"):
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scheduler.set_timesteps(num_inference_steps)
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scheduler_pt.set_timesteps(num_inference_steps)
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elif num_inference_steps is not None and not hasattr(scheduler, "set_timesteps"):
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kwargs["num_inference_steps"] = num_inference_steps
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output = scheduler.step(residual, 1, sample, **kwargs)["prev_sample"]
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output_pt = scheduler_pt.step(residual_pt, 1, sample_pt, **kwargs)["prev_sample"]
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assert np.sum(np.abs(output - output_pt.numpy())) < 1e-4, "Scheduler outputs are not identical"
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class DDPMSchedulerTest(SchedulerCommonTest):
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scheduler_classes = (DDPMScheduler,)
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def get_scheduler_config(self, **kwargs):
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config = {
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"num_train_timesteps": 1000,
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"beta_start": 0.0001,
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"beta_end": 0.02,
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"beta_schedule": "linear",
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"variance_type": "fixed_small",
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"clip_sample": True,
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"tensor_format": "pt",
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}
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config.update(**kwargs)
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return config
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def test_timesteps(self):
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for timesteps in [1, 5, 100, 1000]:
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self.check_over_configs(num_train_timesteps=timesteps)
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def test_betas(self):
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for beta_start, beta_end in zip([0.0001, 0.001, 0.01, 0.1], [0.002, 0.02, 0.2, 2]):
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self.check_over_configs(beta_start=beta_start, beta_end=beta_end)
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def test_schedules(self):
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for schedule in ["linear", "squaredcos_cap_v2"]:
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self.check_over_configs(beta_schedule=schedule)
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def test_variance_type(self):
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for variance in ["fixed_small", "fixed_large", "other"]:
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self.check_over_configs(variance_type=variance)
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def test_clip_sample(self):
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for clip_sample in [True, False]:
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self.check_over_configs(clip_sample=clip_sample)
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def test_time_indices(self):
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for t in [0, 500, 999]:
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self.check_over_forward(time_step=t)
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def test_variance(self):
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scheduler_class = self.scheduler_classes[0]
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scheduler_config = self.get_scheduler_config()
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scheduler = scheduler_class(**scheduler_config)
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assert torch.sum(torch.abs(scheduler._get_variance(0) - 0.0)) < 1e-5
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assert torch.sum(torch.abs(scheduler._get_variance(487) - 0.00979)) < 1e-5
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assert torch.sum(torch.abs(scheduler._get_variance(999) - 0.02)) < 1e-5
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# TODO Make DDPM Numpy compatible
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def test_pytorch_equal_numpy(self):
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pass
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def test_full_loop_no_noise(self):
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scheduler_class = self.scheduler_classes[0]
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scheduler_config = self.get_scheduler_config()
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scheduler = scheduler_class(**scheduler_config)
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num_trained_timesteps = len(scheduler)
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model = self.dummy_model()
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sample = self.dummy_sample_deter
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for t in reversed(range(num_trained_timesteps)):
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# 1. predict noise residual
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residual = model(sample, t)
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# 2. predict previous mean of sample x_t-1
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pred_prev_sample = scheduler.step(residual, t, sample)["prev_sample"]
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# if t > 0:
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# noise = self.dummy_sample_deter
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# variance = scheduler.get_variance(t) ** (0.5) * noise
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#
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# sample = pred_prev_sample + variance
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sample = pred_prev_sample
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result_sum = torch.sum(torch.abs(sample))
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result_mean = torch.mean(torch.abs(sample))
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assert abs(result_sum.item() - 259.0883) < 1e-2
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assert abs(result_mean.item() - 0.3374) < 1e-3
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class DDIMSchedulerTest(SchedulerCommonTest):
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scheduler_classes = (DDIMScheduler,)
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forward_default_kwargs = (("eta", 0.0), ("num_inference_steps", 50))
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def get_scheduler_config(self, **kwargs):
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config = {
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"num_train_timesteps": 1000,
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"beta_start": 0.0001,
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"beta_end": 0.02,
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"beta_schedule": "linear",
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"clip_sample": True,
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}
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config.update(**kwargs)
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return config
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def test_timesteps(self):
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for timesteps in [100, 500, 1000]:
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self.check_over_configs(num_train_timesteps=timesteps)
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def test_betas(self):
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for beta_start, beta_end in zip([0.0001, 0.001, 0.01, 0.1], [0.002, 0.02, 0.2, 2]):
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self.check_over_configs(beta_start=beta_start, beta_end=beta_end)
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def test_schedules(self):
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for schedule in ["linear", "squaredcos_cap_v2"]:
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self.check_over_configs(beta_schedule=schedule)
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def test_clip_sample(self):
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for clip_sample in [True, False]:
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self.check_over_configs(clip_sample=clip_sample)
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def test_time_indices(self):
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for t in [1, 10, 49]:
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self.check_over_forward(time_step=t)
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def test_inference_steps(self):
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for t, num_inference_steps in zip([1, 10, 50], [10, 50, 500]):
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self.check_over_forward(num_inference_steps=num_inference_steps)
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def test_eta(self):
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for t, eta in zip([1, 10, 49], [0.0, 0.5, 1.0]):
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self.check_over_forward(time_step=t, eta=eta)
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def test_variance(self):
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scheduler_class = self.scheduler_classes[0]
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scheduler_config = self.get_scheduler_config()
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scheduler = scheduler_class(**scheduler_config)
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assert torch.sum(torch.abs(scheduler._get_variance(0, 0) - 0.0)) < 1e-5
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assert torch.sum(torch.abs(scheduler._get_variance(420, 400) - 0.14771)) < 1e-5
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assert torch.sum(torch.abs(scheduler._get_variance(980, 960) - 0.32460)) < 1e-5
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assert torch.sum(torch.abs(scheduler._get_variance(0, 0) - 0.0)) < 1e-5
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assert torch.sum(torch.abs(scheduler._get_variance(487, 486) - 0.00979)) < 1e-5
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assert torch.sum(torch.abs(scheduler._get_variance(999, 998) - 0.02)) < 1e-5
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def test_full_loop_no_noise(self):
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scheduler_class = self.scheduler_classes[0]
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scheduler_config = self.get_scheduler_config()
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scheduler = scheduler_class(**scheduler_config)
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num_inference_steps, eta = 10, 0.0
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model = self.dummy_model()
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sample = self.dummy_sample_deter
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scheduler.set_timesteps(num_inference_steps)
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for t in scheduler.timesteps:
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residual = model(sample, t)
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sample = scheduler.step(residual, t, sample, eta)["prev_sample"]
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result_sum = torch.sum(torch.abs(sample))
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result_mean = torch.mean(torch.abs(sample))
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assert abs(result_sum.item() - 172.0067) < 1e-2
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assert abs(result_mean.item() - 0.223967) < 1e-3
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class PNDMSchedulerTest(SchedulerCommonTest):
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scheduler_classes = (PNDMScheduler,)
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forward_default_kwargs = (("num_inference_steps", 50),)
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def get_scheduler_config(self, **kwargs):
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config = {
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"num_train_timesteps": 1000,
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"beta_start": 0.0001,
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"beta_end": 0.02,
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"beta_schedule": "linear",
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}
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config.update(**kwargs)
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return config
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def check_over_configs(self, time_step=0, **config):
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kwargs = dict(self.forward_default_kwargs)
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num_inference_steps = kwargs.pop("num_inference_steps", None)
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sample = self.dummy_sample
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residual = 0.1 * sample
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dummy_past_residuals = [residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]
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for scheduler_class in self.scheduler_classes:
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scheduler_config = self.get_scheduler_config(**config)
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scheduler = scheduler_class(**scheduler_config)
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scheduler.set_timesteps(num_inference_steps)
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# copy over dummy past residuals
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scheduler.ets = dummy_past_residuals[:]
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with tempfile.TemporaryDirectory() as tmpdirname:
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scheduler.save_config(tmpdirname)
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new_scheduler = scheduler_class.from_config(tmpdirname)
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new_scheduler.set_timesteps(num_inference_steps)
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# copy over dummy past residuals
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new_scheduler.ets = dummy_past_residuals[:]
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output = scheduler.step_prk(residual, time_step, sample, **kwargs)["prev_sample"]
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new_output = new_scheduler.step_prk(residual, time_step, sample, **kwargs)["prev_sample"]
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assert torch.sum(torch.abs(output - new_output)) < 1e-5, "Scheduler outputs are not identical"
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output = scheduler.step_plms(residual, time_step, sample, **kwargs)["prev_sample"]
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new_output = new_scheduler.step_plms(residual, time_step, sample, **kwargs)["prev_sample"]
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assert torch.sum(torch.abs(output - new_output)) < 1e-5, "Scheduler outputs are not identical"
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def test_from_pretrained_save_pretrained(self):
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pass
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def check_over_forward(self, time_step=0, **forward_kwargs):
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kwargs = dict(self.forward_default_kwargs)
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num_inference_steps = kwargs.pop("num_inference_steps", None)
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sample = self.dummy_sample
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residual = 0.1 * sample
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dummy_past_residuals = [residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]
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for scheduler_class in self.scheduler_classes:
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scheduler_config = self.get_scheduler_config()
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scheduler = scheduler_class(**scheduler_config)
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scheduler.set_timesteps(num_inference_steps)
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# copy over dummy past residuals
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scheduler.ets = dummy_past_residuals[:]
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with tempfile.TemporaryDirectory() as tmpdirname:
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scheduler.save_config(tmpdirname)
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new_scheduler = scheduler_class.from_config(tmpdirname)
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# copy over dummy past residuals
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new_scheduler.ets = dummy_past_residuals[:]
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new_scheduler.set_timesteps(num_inference_steps)
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output = scheduler.step_prk(residual, time_step, sample, **kwargs)["prev_sample"]
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new_output = new_scheduler.step_prk(residual, time_step, sample, **kwargs)["prev_sample"]
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assert torch.sum(torch.abs(output - new_output)) < 1e-5, "Scheduler outputs are not identical"
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output = scheduler.step_plms(residual, time_step, sample, **kwargs)["prev_sample"]
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new_output = new_scheduler.step_plms(residual, time_step, sample, **kwargs)["prev_sample"]
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assert torch.sum(torch.abs(output - new_output)) < 1e-5, "Scheduler outputs are not identical"
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def test_pytorch_equal_numpy(self):
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kwargs = dict(self.forward_default_kwargs)
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num_inference_steps = kwargs.pop("num_inference_steps", None)
|
|
|
|
for scheduler_class in self.scheduler_classes:
|
|
sample_pt = self.dummy_sample
|
|
residual_pt = 0.1 * sample_pt
|
|
dummy_past_residuals_pt = [residual_pt + 0.2, residual_pt + 0.15, residual_pt + 0.1, residual_pt + 0.05]
|
|
|
|
sample = sample_pt.numpy()
|
|
residual = 0.1 * sample
|
|
dummy_past_residuals = [residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]
|
|
|
|
scheduler_config = self.get_scheduler_config()
|
|
scheduler = scheduler_class(tensor_format="np", **scheduler_config)
|
|
# copy over dummy past residuals
|
|
scheduler.ets = dummy_past_residuals[:]
|
|
|
|
scheduler_pt = scheduler_class(tensor_format="pt", **scheduler_config)
|
|
# copy over dummy past residuals
|
|
scheduler_pt.ets = dummy_past_residuals_pt[:]
|
|
|
|
if num_inference_steps is not None and hasattr(scheduler, "set_timesteps"):
|
|
scheduler.set_timesteps(num_inference_steps)
|
|
scheduler_pt.set_timesteps(num_inference_steps)
|
|
elif num_inference_steps is not None and not hasattr(scheduler, "set_timesteps"):
|
|
kwargs["num_inference_steps"] = num_inference_steps
|
|
|
|
output = scheduler.step_prk(residual, 1, sample, **kwargs)["prev_sample"]
|
|
output_pt = scheduler_pt.step_prk(residual_pt, 1, sample_pt, **kwargs)["prev_sample"]
|
|
assert np.sum(np.abs(output - output_pt.numpy())) < 1e-4, "Scheduler outputs are not identical"
|
|
|
|
output = scheduler.step_plms(residual, 1, sample, **kwargs)["prev_sample"]
|
|
output_pt = scheduler_pt.step_plms(residual_pt, 1, sample_pt, **kwargs)["prev_sample"]
|
|
|
|
assert np.sum(np.abs(output - output_pt.numpy())) < 1e-4, "Scheduler outputs are not identical"
|
|
|
|
def test_step_shape(self):
|
|
kwargs = dict(self.forward_default_kwargs)
|
|
|
|
num_inference_steps = kwargs.pop("num_inference_steps", None)
|
|
|
|
for scheduler_class in self.scheduler_classes:
|
|
scheduler_config = self.get_scheduler_config()
|
|
scheduler = scheduler_class(**scheduler_config)
|
|
|
|
sample = self.dummy_sample
|
|
residual = 0.1 * sample
|
|
# copy over dummy past residuals
|
|
dummy_past_residuals = [residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]
|
|
scheduler.ets = dummy_past_residuals[:]
|
|
|
|
if num_inference_steps is not None and hasattr(scheduler, "set_timesteps"):
|
|
scheduler.set_timesteps(num_inference_steps)
|
|
elif num_inference_steps is not None and not hasattr(scheduler, "set_timesteps"):
|
|
kwargs["num_inference_steps"] = num_inference_steps
|
|
|
|
output_0 = scheduler.step_prk(residual, 0, sample, **kwargs)["prev_sample"]
|
|
output_1 = scheduler.step_prk(residual, 1, sample, **kwargs)["prev_sample"]
|
|
|
|
self.assertEqual(output_0.shape, sample.shape)
|
|
self.assertEqual(output_0.shape, output_1.shape)
|
|
|
|
output_0 = scheduler.step_plms(residual, 0, sample, **kwargs)["prev_sample"]
|
|
output_1 = scheduler.step_plms(residual, 1, sample, **kwargs)["prev_sample"]
|
|
|
|
self.assertEqual(output_0.shape, sample.shape)
|
|
self.assertEqual(output_0.shape, output_1.shape)
|
|
|
|
def test_timesteps(self):
|
|
for timesteps in [100, 1000]:
|
|
self.check_over_configs(num_train_timesteps=timesteps)
|
|
|
|
def test_betas(self):
|
|
for beta_start, beta_end in zip([0.0001, 0.001, 0.01], [0.002, 0.02, 0.2]):
|
|
self.check_over_configs(beta_start=beta_start, beta_end=beta_end)
|
|
|
|
def test_schedules(self):
|
|
for schedule in ["linear", "squaredcos_cap_v2"]:
|
|
self.check_over_configs(beta_schedule=schedule)
|
|
|
|
def test_time_indices(self):
|
|
for t in [1, 5, 10]:
|
|
self.check_over_forward(time_step=t)
|
|
|
|
def test_inference_steps(self):
|
|
for t, num_inference_steps in zip([1, 5, 10], [10, 50, 100]):
|
|
self.check_over_forward(time_step=t, num_inference_steps=num_inference_steps)
|
|
|
|
def test_inference_plms_no_past_residuals(self):
|
|
with self.assertRaises(ValueError):
|
|
scheduler_class = self.scheduler_classes[0]
|
|
scheduler_config = self.get_scheduler_config()
|
|
scheduler = scheduler_class(**scheduler_config)
|
|
|
|
scheduler.step_plms(self.dummy_sample, 1, self.dummy_sample)["prev_sample"]
|
|
|
|
def test_full_loop_no_noise(self):
|
|
scheduler_class = self.scheduler_classes[0]
|
|
scheduler_config = self.get_scheduler_config()
|
|
scheduler = scheduler_class(**scheduler_config)
|
|
|
|
num_inference_steps = 10
|
|
model = self.dummy_model()
|
|
sample = self.dummy_sample_deter
|
|
scheduler.set_timesteps(num_inference_steps)
|
|
|
|
for i, t in enumerate(scheduler.prk_timesteps):
|
|
residual = model(sample, t)
|
|
sample = scheduler.step_prk(residual, i, sample)["prev_sample"]
|
|
|
|
for i, t in enumerate(scheduler.plms_timesteps):
|
|
residual = model(sample, t)
|
|
sample = scheduler.step_plms(residual, i, sample)["prev_sample"]
|
|
|
|
result_sum = torch.sum(torch.abs(sample))
|
|
result_mean = torch.mean(torch.abs(sample))
|
|
|
|
assert abs(result_sum.item() - 199.1169) < 1e-2
|
|
assert abs(result_mean.item() - 0.2593) < 1e-3
|
|
|
|
|
|
class ScoreSdeVeSchedulerTest(unittest.TestCase):
|
|
# TODO adapt with class SchedulerCommonTest (scheduler needs Numpy Integration)
|
|
scheduler_classes = (ScoreSdeVeScheduler,)
|
|
forward_default_kwargs = (("seed", 0),)
|
|
|
|
@property
|
|
def dummy_sample(self):
|
|
batch_size = 4
|
|
num_channels = 3
|
|
height = 8
|
|
width = 8
|
|
|
|
sample = torch.rand((batch_size, num_channels, height, width))
|
|
|
|
return sample
|
|
|
|
@property
|
|
def dummy_sample_deter(self):
|
|
batch_size = 4
|
|
num_channels = 3
|
|
height = 8
|
|
width = 8
|
|
|
|
num_elems = batch_size * num_channels * height * width
|
|
sample = torch.arange(num_elems)
|
|
sample = sample.reshape(num_channels, height, width, batch_size)
|
|
sample = sample / num_elems
|
|
sample = sample.permute(3, 0, 1, 2)
|
|
|
|
return sample
|
|
|
|
def dummy_model(self):
|
|
def model(sample, t, *args):
|
|
return sample * t / (t + 1)
|
|
|
|
return model
|
|
|
|
def get_scheduler_config(self, **kwargs):
|
|
config = {
|
|
"num_train_timesteps": 2000,
|
|
"snr": 0.15,
|
|
"sigma_min": 0.01,
|
|
"sigma_max": 1348,
|
|
"sampling_eps": 1e-5,
|
|
"tensor_format": "pt", # TODO add test for tensor formats
|
|
}
|
|
|
|
config.update(**kwargs)
|
|
return config
|
|
|
|
def check_over_configs(self, time_step=0, **config):
|
|
kwargs = dict(self.forward_default_kwargs)
|
|
|
|
for scheduler_class in self.scheduler_classes:
|
|
sample = self.dummy_sample
|
|
residual = 0.1 * sample
|
|
|
|
scheduler_config = self.get_scheduler_config(**config)
|
|
scheduler = scheduler_class(**scheduler_config)
|
|
|
|
with tempfile.TemporaryDirectory() as tmpdirname:
|
|
scheduler.save_config(tmpdirname)
|
|
new_scheduler = scheduler_class.from_config(tmpdirname)
|
|
|
|
output = scheduler.step_pred(residual, time_step, sample, **kwargs)["prev_sample"]
|
|
new_output = new_scheduler.step_pred(residual, time_step, sample, **kwargs)["prev_sample"]
|
|
|
|
assert torch.sum(torch.abs(output - new_output)) < 1e-5, "Scheduler outputs are not identical"
|
|
|
|
output = scheduler.step_correct(residual, sample, **kwargs)["prev_sample"]
|
|
new_output = new_scheduler.step_correct(residual, sample, **kwargs)["prev_sample"]
|
|
|
|
assert torch.sum(torch.abs(output - new_output)) < 1e-5, "Scheduler correction are not identical"
|
|
|
|
def check_over_forward(self, time_step=0, **forward_kwargs):
|
|
kwargs = dict(self.forward_default_kwargs)
|
|
kwargs.update(forward_kwargs)
|
|
|
|
for scheduler_class in self.scheduler_classes:
|
|
sample = self.dummy_sample
|
|
residual = 0.1 * sample
|
|
|
|
scheduler_config = self.get_scheduler_config()
|
|
scheduler = scheduler_class(**scheduler_config)
|
|
|
|
with tempfile.TemporaryDirectory() as tmpdirname:
|
|
scheduler.save_config(tmpdirname)
|
|
new_scheduler = scheduler_class.from_config(tmpdirname)
|
|
|
|
output = scheduler.step_pred(residual, time_step, sample, **kwargs)["prev_sample"]
|
|
new_output = new_scheduler.step_pred(residual, time_step, sample, **kwargs)["prev_sample"]
|
|
|
|
assert torch.sum(torch.abs(output - new_output)) < 1e-5, "Scheduler outputs are not identical"
|
|
|
|
output = scheduler.step_correct(residual, sample, **kwargs)["prev_sample"]
|
|
new_output = new_scheduler.step_correct(residual, sample, **kwargs)["prev_sample"]
|
|
|
|
assert torch.sum(torch.abs(output - new_output)) < 1e-5, "Scheduler correction are not identical"
|
|
|
|
def test_timesteps(self):
|
|
for timesteps in [10, 100, 1000]:
|
|
self.check_over_configs(num_train_timesteps=timesteps)
|
|
|
|
def test_sigmas(self):
|
|
for sigma_min, sigma_max in zip([0.0001, 0.001, 0.01], [1, 100, 1000]):
|
|
self.check_over_configs(sigma_min=sigma_min, sigma_max=sigma_max)
|
|
|
|
def test_time_indices(self):
|
|
for t in [0.1, 0.5, 0.75]:
|
|
self.check_over_forward(time_step=t)
|
|
|
|
def test_full_loop_no_noise(self):
|
|
kwargs = dict(self.forward_default_kwargs)
|
|
|
|
scheduler_class = self.scheduler_classes[0]
|
|
scheduler_config = self.get_scheduler_config()
|
|
scheduler = scheduler_class(**scheduler_config)
|
|
|
|
num_inference_steps = 3
|
|
|
|
model = self.dummy_model()
|
|
sample = self.dummy_sample_deter
|
|
|
|
scheduler.set_sigmas(num_inference_steps)
|
|
scheduler.set_timesteps(num_inference_steps)
|
|
|
|
for i, t in enumerate(scheduler.timesteps):
|
|
sigma_t = scheduler.sigmas[i]
|
|
|
|
for _ in range(scheduler.correct_steps):
|
|
with torch.no_grad():
|
|
model_output = model(sample, sigma_t)
|
|
sample = scheduler.step_correct(model_output, sample, **kwargs)["prev_sample"]
|
|
|
|
with torch.no_grad():
|
|
model_output = model(sample, sigma_t)
|
|
|
|
output = scheduler.step_pred(model_output, t, sample, **kwargs)
|
|
sample, _ = output["prev_sample"], output["prev_sample_mean"]
|
|
|
|
result_sum = torch.sum(torch.abs(sample))
|
|
result_mean = torch.mean(torch.abs(sample))
|
|
|
|
assert abs(result_sum.item() - 14379591680.0) < 1e-2
|
|
assert abs(result_mean.item() - 18723426.0) < 1e-3
|
|
|
|
def test_step_shape(self):
|
|
kwargs = dict(self.forward_default_kwargs)
|
|
|
|
num_inference_steps = kwargs.pop("num_inference_steps", None)
|
|
|
|
for scheduler_class in self.scheduler_classes:
|
|
scheduler_config = self.get_scheduler_config()
|
|
scheduler = scheduler_class(**scheduler_config)
|
|
|
|
sample = self.dummy_sample
|
|
residual = 0.1 * sample
|
|
|
|
if num_inference_steps is not None and hasattr(scheduler, "set_timesteps"):
|
|
scheduler.set_timesteps(num_inference_steps)
|
|
elif num_inference_steps is not None and not hasattr(scheduler, "set_timesteps"):
|
|
kwargs["num_inference_steps"] = num_inference_steps
|
|
|
|
output_0 = scheduler.step_pred(residual, 0, sample, **kwargs)["prev_sample"]
|
|
output_1 = scheduler.step_pred(residual, 1, sample, **kwargs)["prev_sample"]
|
|
|
|
self.assertEqual(output_0.shape, sample.shape)
|
|
self.assertEqual(output_0.shape, output_1.shape)
|