[Tests] Split scheduler tests (#2630)

* up

* correct some

* up

* finish

tests/schedulers/test_scheduler_ddim.py

+import torch
+
+from diffusers import DDIMScheduler
+
+from .test_schedulers import SchedulerCommonTest
+
+
+class DDIMSchedulerTest(SchedulerCommonTest):
+    scheduler_classes = (DDIMScheduler,)
+    forward_default_kwargs = (("eta", 0.0), ("num_inference_steps", 50))
+
+    def get_scheduler_config(self, **kwargs):
+        config = {
+            "num_train_timesteps": 1000,
+            "beta_start": 0.0001,
+            "beta_end": 0.02,
+            "beta_schedule": "linear",
+            "clip_sample": True,
+        }
+
+        config.update(**kwargs)
+        return config
+
+    def full_loop(self, **config):
+        scheduler_class = self.scheduler_classes[0]
+        scheduler_config = self.get_scheduler_config(**config)
+        scheduler = scheduler_class(**scheduler_config)
+
+        num_inference_steps, eta = 10, 0.0
+
+        model = self.dummy_model()
+        sample = self.dummy_sample_deter
+
+        scheduler.set_timesteps(num_inference_steps)
+
+        for t in scheduler.timesteps:
+            residual = model(sample, t)
+            sample = scheduler.step(residual, t, sample, eta).prev_sample
+
+        return sample
+
+    def test_timesteps(self):
+        for timesteps in [100, 500, 1000]:
+            self.check_over_configs(num_train_timesteps=timesteps)
+
+    def test_steps_offset(self):
+        for steps_offset in [0, 1]:
+            self.check_over_configs(steps_offset=steps_offset)
+
+        scheduler_class = self.scheduler_classes[0]
+        scheduler_config = self.get_scheduler_config(steps_offset=1)
+        scheduler = scheduler_class(**scheduler_config)
+        scheduler.set_timesteps(5)
+        assert torch.equal(scheduler.timesteps, torch.LongTensor([801, 601, 401, 201, 1]))
+
+    def test_betas(self):
+        for beta_start, beta_end in zip([0.0001, 0.001, 0.01, 0.1], [0.002, 0.02, 0.2, 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_prediction_type(self):
+        for prediction_type in ["epsilon", "v_prediction"]:
+            self.check_over_configs(prediction_type=prediction_type)
+
+    def test_clip_sample(self):
+        for clip_sample in [True, False]:
+            self.check_over_configs(clip_sample=clip_sample)
+
+    def test_thresholding(self):
+        self.check_over_configs(thresholding=False)
+        for threshold in [0.5, 1.0, 2.0]:
+            for prediction_type in ["epsilon", "v_prediction"]:
+                self.check_over_configs(
+                    thresholding=True,
+                    prediction_type=prediction_type,
+                    sample_max_value=threshold,
+                )
+
+    def test_time_indices(self):
+        for t in [1, 10, 49]:
+            self.check_over_forward(time_step=t)
+
+    def test_inference_steps(self):
+        for t, num_inference_steps in zip([1, 10, 50], [10, 50, 500]):
+            self.check_over_forward(time_step=t, num_inference_steps=num_inference_steps)
+
+    def test_eta(self):
+        for t, eta in zip([1, 10, 49], [0.0, 0.5, 1.0]):
+            self.check_over_forward(time_step=t, eta=eta)
+
+    def test_variance(self):
+        scheduler_class = self.scheduler_classes[0]
+        scheduler_config = self.get_scheduler_config()
+        scheduler = scheduler_class(**scheduler_config)
+
+        assert torch.sum(torch.abs(scheduler._get_variance(0, 0) - 0.0)) < 1e-5
+        assert torch.sum(torch.abs(scheduler._get_variance(420, 400) - 0.14771)) < 1e-5
+        assert torch.sum(torch.abs(scheduler._get_variance(980, 960) - 0.32460)) < 1e-5
+        assert torch.sum(torch.abs(scheduler._get_variance(0, 0) - 0.0)) < 1e-5
+        assert torch.sum(torch.abs(scheduler._get_variance(487, 486) - 0.00979)) < 1e-5
+        assert torch.sum(torch.abs(scheduler._get_variance(999, 998) - 0.02)) < 1e-5
+
+    def test_full_loop_no_noise(self):
+        sample = self.full_loop()
+
+        result_sum = torch.sum(torch.abs(sample))
+        result_mean = torch.mean(torch.abs(sample))
+
+        assert abs(result_sum.item() - 172.0067) < 1e-2
+        assert abs(result_mean.item() - 0.223967) < 1e-3
+
+    def test_full_loop_with_v_prediction(self):
+        sample = self.full_loop(prediction_type="v_prediction")
+
+        result_sum = torch.sum(torch.abs(sample))
+        result_mean = torch.mean(torch.abs(sample))
+
+        assert abs(result_sum.item() - 52.5302) < 1e-2
+        assert abs(result_mean.item() - 0.0684) < 1e-3
+
+    def test_full_loop_with_set_alpha_to_one(self):
+        # We specify different beta, so that the first alpha is 0.99
+        sample = self.full_loop(set_alpha_to_one=True, beta_start=0.01)
+        result_sum = torch.sum(torch.abs(sample))
+        result_mean = torch.mean(torch.abs(sample))
+
+        assert abs(result_sum.item() - 149.8295) < 1e-2
+        assert abs(result_mean.item() - 0.1951) < 1e-3
+
+    def test_full_loop_with_no_set_alpha_to_one(self):
+        # We specify different beta, so that the first alpha is 0.99
+        sample = self.full_loop(set_alpha_to_one=False, beta_start=0.01)
+        result_sum = torch.sum(torch.abs(sample))
+        result_mean = torch.mean(torch.abs(sample))
+
+        assert abs(result_sum.item() - 149.0784) < 1e-2
+        assert abs(result_mean.item() - 0.1941) < 1e-3

tests/schedulers/test_scheduler_ddpm.py

+import torch
+
+from diffusers import DDPMScheduler
+
+from .test_schedulers import SchedulerCommonTest
+
+
+class DDPMSchedulerTest(SchedulerCommonTest):
+    scheduler_classes = (DDPMScheduler,)
+
+    def get_scheduler_config(self, **kwargs):
+        config = {
+            "num_train_timesteps": 1000,
+            "beta_start": 0.0001,
+            "beta_end": 0.02,
+            "beta_schedule": "linear",
+            "variance_type": "fixed_small",
+            "clip_sample": True,
+        }
+
+        config.update(**kwargs)
+        return config
+
+    def test_timesteps(self):
+        for timesteps in [1, 5, 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, 0.1], [0.002, 0.02, 0.2, 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_variance_type(self):
+        for variance in ["fixed_small", "fixed_large", "other"]:
+            self.check_over_configs(variance_type=variance)
+
+    def test_clip_sample(self):
+        for clip_sample in [True, False]:
+            self.check_over_configs(clip_sample=clip_sample)
+
+    def test_thresholding(self):
+        self.check_over_configs(thresholding=False)
+        for threshold in [0.5, 1.0, 2.0]:
+            for prediction_type in ["epsilon", "sample", "v_prediction"]:
+                self.check_over_configs(
+                    thresholding=True,
+                    prediction_type=prediction_type,
+                    sample_max_value=threshold,
+                )
+
+    def test_prediction_type(self):
+        for prediction_type in ["epsilon", "sample", "v_prediction"]:
+            self.check_over_configs(prediction_type=prediction_type)
+
+    def test_time_indices(self):
+        for t in [0, 500, 999]:
+            self.check_over_forward(time_step=t)
+
+    def test_variance(self):
+        scheduler_class = self.scheduler_classes[0]
+        scheduler_config = self.get_scheduler_config()
+        scheduler = scheduler_class(**scheduler_config)
+
+        assert torch.sum(torch.abs(scheduler._get_variance(0) - 0.0)) < 1e-5
+        assert torch.sum(torch.abs(scheduler._get_variance(487) - 0.00979)) < 1e-5
+        assert torch.sum(torch.abs(scheduler._get_variance(999) - 0.02)) < 1e-5
+
+    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_trained_timesteps = len(scheduler)
+
+        model = self.dummy_model()
+        sample = self.dummy_sample_deter
+        generator = torch.manual_seed(0)
+
+        for t in reversed(range(num_trained_timesteps)):
+            # 1. predict noise residual
+            residual = model(sample, t)
+
+            # 2. predict previous mean of sample x_t-1
+            pred_prev_sample = scheduler.step(residual, t, sample, generator=generator).prev_sample
+
+            # if t > 0:
+            #     noise = self.dummy_sample_deter
+            #     variance = scheduler.get_variance(t) ** (0.5) * noise
+            #
+            # sample = pred_prev_sample + variance
+            sample = pred_prev_sample
+
+        result_sum = torch.sum(torch.abs(sample))
+        result_mean = torch.mean(torch.abs(sample))
+
+        assert abs(result_sum.item() - 258.9606) < 1e-2
+        assert abs(result_mean.item() - 0.3372) < 1e-3
+
+    def test_full_loop_with_v_prediction(self):
+        scheduler_class = self.scheduler_classes[0]
+        scheduler_config = self.get_scheduler_config(prediction_type="v_prediction")
+        scheduler = scheduler_class(**scheduler_config)
+
+        num_trained_timesteps = len(scheduler)
+
+        model = self.dummy_model()
+        sample = self.dummy_sample_deter
+        generator = torch.manual_seed(0)
+
+        for t in reversed(range(num_trained_timesteps)):
+            # 1. predict noise residual
+            residual = model(sample, t)
+
+            # 2. predict previous mean of sample x_t-1
+            pred_prev_sample = scheduler.step(residual, t, sample, generator=generator).prev_sample
+
+            # if t > 0:
+            #     noise = self.dummy_sample_deter
+            #     variance = scheduler.get_variance(t) ** (0.5) * noise
+            #
+            # sample = pred_prev_sample + variance
+            sample = pred_prev_sample
+
+        result_sum = torch.sum(torch.abs(sample))
+        result_mean = torch.mean(torch.abs(sample))
+
+        assert abs(result_sum.item() - 202.0296) < 1e-2
+        assert abs(result_mean.item() - 0.2631) < 1e-3

tests/schedulers/test_scheduler_deis.py

+import tempfile
+
+import torch
+
+from diffusers import (
+    DEISMultistepScheduler,
+    DPMSolverMultistepScheduler,
+    DPMSolverSinglestepScheduler,
+    UniPCMultistepScheduler,
+)
+
+from .test_schedulers import SchedulerCommonTest
+
+
+class DEISMultistepSchedulerTest(SchedulerCommonTest):
+    scheduler_classes = (DEISMultistepScheduler,)
+    forward_default_kwargs = (("num_inference_steps", 25),)
+
+    def get_scheduler_config(self, **kwargs):
+        config = {
+            "num_train_timesteps": 1000,
+            "beta_start": 0.0001,
+            "beta_end": 0.02,
+            "beta_schedule": "linear",
+            "solver_order": 2,
+        }
+
+        config.update(**kwargs)
+        return config
+
+    def check_over_configs(self, time_step=0, **config):
+        kwargs = dict(self.forward_default_kwargs)
+        num_inference_steps = kwargs.pop("num_inference_steps", None)
+        sample = self.dummy_sample
+        residual = 0.1 * sample
+        dummy_past_residuals = [residual + 0.2, residual + 0.15, residual + 0.10]
+
+        for scheduler_class in self.scheduler_classes:
+            scheduler_config = self.get_scheduler_config(**config)
+            scheduler = scheduler_class(**scheduler_config)
+            scheduler.set_timesteps(num_inference_steps)
+            # copy over dummy past residuals
+            scheduler.model_outputs = dummy_past_residuals[: scheduler.config.solver_order]
+
+            with tempfile.TemporaryDirectory() as tmpdirname:
+                scheduler.save_config(tmpdirname)
+                new_scheduler = scheduler_class.from_pretrained(tmpdirname)
+                new_scheduler.set_timesteps(num_inference_steps)
+                # copy over dummy past residuals
+                new_scheduler.model_outputs = dummy_past_residuals[: new_scheduler.config.solver_order]
+
+            output, new_output = sample, sample
+            for t in range(time_step, time_step + scheduler.config.solver_order + 1):
+                output = scheduler.step(residual, t, output, **kwargs).prev_sample
+                new_output = new_scheduler.step(residual, t, new_output, **kwargs).prev_sample
+
+                assert torch.sum(torch.abs(output - new_output)) < 1e-5, "Scheduler outputs are not identical"
+
+    def test_from_save_pretrained(self):
+        pass
+
+    def check_over_forward(self, time_step=0, **forward_kwargs):
+        kwargs = dict(self.forward_default_kwargs)
+        num_inference_steps = kwargs.pop("num_inference_steps", None)
+        sample = self.dummy_sample
+        residual = 0.1 * sample
+        dummy_past_residuals = [residual + 0.2, residual + 0.15, residual + 0.10]
+
+        for scheduler_class in self.scheduler_classes:
+            scheduler_config = self.get_scheduler_config()
+            scheduler = scheduler_class(**scheduler_config)
+            scheduler.set_timesteps(num_inference_steps)
+
+            # copy over dummy past residuals (must be after setting timesteps)
+            scheduler.model_outputs = dummy_past_residuals[: scheduler.config.solver_order]
+
+            with tempfile.TemporaryDirectory() as tmpdirname:
+                scheduler.save_config(tmpdirname)
+                new_scheduler = scheduler_class.from_pretrained(tmpdirname)
+                # copy over dummy past residuals
+                new_scheduler.set_timesteps(num_inference_steps)
+
+                # copy over dummy past residual (must be after setting timesteps)
+                new_scheduler.model_outputs = dummy_past_residuals[: new_scheduler.config.solver_order]
+
+            output = scheduler.step(residual, time_step, sample, **kwargs).prev_sample
+            new_output = new_scheduler.step(residual, time_step, sample, **kwargs).prev_sample
+
+            assert torch.sum(torch.abs(output - new_output)) < 1e-5, "Scheduler outputs are not identical"
+
+    def full_loop(self, scheduler=None, **config):
+        if scheduler is None:
+            scheduler_class = self.scheduler_classes[0]
+            scheduler_config = self.get_scheduler_config(**config)
+            scheduler = scheduler_class(**scheduler_config)
+
+        scheduler_class = self.scheduler_classes[0]
+        scheduler_config = self.get_scheduler_config(**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.timesteps):
+            residual = model(sample, t)
+            sample = scheduler.step(residual, t, sample).prev_sample
+
+        return sample
+
+    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
+
+            # copy over dummy past residuals (must be done after set_timesteps)
+            dummy_past_residuals = [residual + 0.2, residual + 0.15, residual + 0.10]
+            scheduler.model_outputs = dummy_past_residuals[: scheduler.config.solver_order]
+
+            time_step_0 = scheduler.timesteps[5]
+            time_step_1 = scheduler.timesteps[6]
+
+            output_0 = scheduler.step(residual, time_step_0, sample, **kwargs).prev_sample
+            output_1 = scheduler.step(residual, time_step_1, sample, **kwargs).prev_sample
+
+            self.assertEqual(output_0.shape, sample.shape)
+            self.assertEqual(output_0.shape, output_1.shape)
+
+    def test_switch(self):
+        # make sure that iterating over schedulers with same config names gives same results
+        # for defaults
+        scheduler = DEISMultistepScheduler(**self.get_scheduler_config())
+        sample = self.full_loop(scheduler=scheduler)
+        result_mean = torch.mean(torch.abs(sample))
+
+        assert abs(result_mean.item() - 0.23916) < 1e-3
+
+        scheduler = DPMSolverSinglestepScheduler.from_config(scheduler.config)
+        scheduler = DPMSolverMultistepScheduler.from_config(scheduler.config)
+        scheduler = UniPCMultistepScheduler.from_config(scheduler.config)
+        scheduler = DEISMultistepScheduler.from_config(scheduler.config)
+
+        sample = self.full_loop(scheduler=scheduler)
+        result_mean = torch.mean(torch.abs(sample))
+
+        assert abs(result_mean.item() - 0.23916) < 1e-3
+
+    def test_timesteps(self):
+        for timesteps in [25, 50, 100, 999, 1000]:
+            self.check_over_configs(num_train_timesteps=timesteps)
+
+    def test_thresholding(self):
+        self.check_over_configs(thresholding=False)
+        for order in [1, 2, 3]:
+            for solver_type in ["logrho"]:
+                for threshold in [0.5, 1.0, 2.0]:
+                    for prediction_type in ["epsilon", "sample"]:
+                        self.check_over_configs(
+                            thresholding=True,
+                            prediction_type=prediction_type,
+                            sample_max_value=threshold,
+                            algorithm_type="deis",
+                            solver_order=order,
+                            solver_type=solver_type,
+                        )
+
+    def test_prediction_type(self):
+        for prediction_type in ["epsilon", "v_prediction"]:
+            self.check_over_configs(prediction_type=prediction_type)
+
+    def test_solver_order_and_type(self):
+        for algorithm_type in ["deis"]:
+            for solver_type in ["logrho"]:
+                for order in [1, 2, 3]:
+                    for prediction_type in ["epsilon", "sample"]:
+                        self.check_over_configs(
+                            solver_order=order,
+                            solver_type=solver_type,
+                            prediction_type=prediction_type,
+                            algorithm_type=algorithm_type,
+                        )
+                        sample = self.full_loop(
+                            solver_order=order,
+                            solver_type=solver_type,
+                            prediction_type=prediction_type,
+                            algorithm_type=algorithm_type,
+                        )
+                        assert not torch.isnan(sample).any(), "Samples have nan numbers"
+
+    def test_lower_order_final(self):
+        self.check_over_configs(lower_order_final=True)
+        self.check_over_configs(lower_order_final=False)
+
+    def test_inference_steps(self):
+        for num_inference_steps in [1, 2, 3, 5, 10, 50, 100, 999, 1000]:
+            self.check_over_forward(num_inference_steps=num_inference_steps, time_step=0)
+
+    def test_full_loop_no_noise(self):
+        sample = self.full_loop()
+        result_mean = torch.mean(torch.abs(sample))
+
+        assert abs(result_mean.item() - 0.23916) < 1e-3
+
+    def test_full_loop_with_v_prediction(self):
+        sample = self.full_loop(prediction_type="v_prediction")
+        result_mean = torch.mean(torch.abs(sample))
+
+        assert abs(result_mean.item() - 0.091) < 1e-3
+
+    def test_fp16_support(self):
+        scheduler_class = self.scheduler_classes[0]
+        scheduler_config = self.get_scheduler_config(thresholding=True, dynamic_thresholding_ratio=0)
+        scheduler = scheduler_class(**scheduler_config)
+
+        num_inference_steps = 10
+        model = self.dummy_model()
+        sample = self.dummy_sample_deter.half()
+        scheduler.set_timesteps(num_inference_steps)
+
+        for i, t in enumerate(scheduler.timesteps):
+            residual = model(sample, t)
+            sample = scheduler.step(residual, t, sample).prev_sample
+
+        assert sample.dtype == torch.float16

tests/schedulers/test_scheduler_dpm_multi.py

+import tempfile
+
+import torch
+
+from diffusers import (
+    DEISMultistepScheduler,
+    DPMSolverMultistepScheduler,
+    DPMSolverSinglestepScheduler,
+    UniPCMultistepScheduler,
+)
+
+from .test_schedulers import SchedulerCommonTest
+
+
+class DPMSolverMultistepSchedulerTest(SchedulerCommonTest):
+    scheduler_classes = (DPMSolverMultistepScheduler,)
+    forward_default_kwargs = (("num_inference_steps", 25),)
+
+    def get_scheduler_config(self, **kwargs):
+        config = {
+            "num_train_timesteps": 1000,
+            "beta_start": 0.0001,
+            "beta_end": 0.02,
+            "beta_schedule": "linear",
+            "solver_order": 2,
+            "prediction_type": "epsilon",
+            "thresholding": False,
+            "sample_max_value": 1.0,
+            "algorithm_type": "dpmsolver++",
+            "solver_type": "midpoint",
+            "lower_order_final": False,
+        }
+
+        config.update(**kwargs)
+        return config
+
+    def check_over_configs(self, time_step=0, **config):
+        kwargs = dict(self.forward_default_kwargs)
+        num_inference_steps = kwargs.pop("num_inference_steps", None)
+        sample = self.dummy_sample
+        residual = 0.1 * sample
+        dummy_past_residuals = [residual + 0.2, residual + 0.15, residual + 0.10]
+
+        for scheduler_class in self.scheduler_classes:
+            scheduler_config = self.get_scheduler_config(**config)
+            scheduler = scheduler_class(**scheduler_config)
+            scheduler.set_timesteps(num_inference_steps)
+            # copy over dummy past residuals
+            scheduler.model_outputs = dummy_past_residuals[: scheduler.config.solver_order]
+
+            with tempfile.TemporaryDirectory() as tmpdirname:
+                scheduler.save_config(tmpdirname)
+                new_scheduler = scheduler_class.from_pretrained(tmpdirname)
+                new_scheduler.set_timesteps(num_inference_steps)
+                # copy over dummy past residuals
+                new_scheduler.model_outputs = dummy_past_residuals[: new_scheduler.config.solver_order]
+
+            output, new_output = sample, sample
+            for t in range(time_step, time_step + scheduler.config.solver_order + 1):
+                output = scheduler.step(residual, t, output, **kwargs).prev_sample
+                new_output = new_scheduler.step(residual, t, new_output, **kwargs).prev_sample
+
+                assert torch.sum(torch.abs(output - new_output)) < 1e-5, "Scheduler outputs are not identical"
+
+    def test_from_save_pretrained(self):
+        pass
+
+    def check_over_forward(self, time_step=0, **forward_kwargs):
+        kwargs = dict(self.forward_default_kwargs)
+        num_inference_steps = kwargs.pop("num_inference_steps", None)
+        sample = self.dummy_sample
+        residual = 0.1 * sample
+        dummy_past_residuals = [residual + 0.2, residual + 0.15, residual + 0.10]
+
+        for scheduler_class in self.scheduler_classes:
+            scheduler_config = self.get_scheduler_config()
+            scheduler = scheduler_class(**scheduler_config)
+            scheduler.set_timesteps(num_inference_steps)
+
+            # copy over dummy past residuals (must be after setting timesteps)
+            scheduler.model_outputs = dummy_past_residuals[: scheduler.config.solver_order]
+
+            with tempfile.TemporaryDirectory() as tmpdirname:
+                scheduler.save_config(tmpdirname)
+                new_scheduler = scheduler_class.from_pretrained(tmpdirname)
+                # copy over dummy past residuals
+                new_scheduler.set_timesteps(num_inference_steps)
+
+                # copy over dummy past residual (must be after setting timesteps)
+                new_scheduler.model_outputs = dummy_past_residuals[: new_scheduler.config.solver_order]
+
+            output = scheduler.step(residual, time_step, sample, **kwargs).prev_sample
+            new_output = new_scheduler.step(residual, time_step, sample, **kwargs).prev_sample
+
+            assert torch.sum(torch.abs(output - new_output)) < 1e-5, "Scheduler outputs are not identical"
+
+    def full_loop(self, scheduler=None, **config):
+        if scheduler is None:
+            scheduler_class = self.scheduler_classes[0]
+            scheduler_config = self.get_scheduler_config(**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.timesteps):
+            residual = model(sample, t)
+            sample = scheduler.step(residual, t, sample).prev_sample
+
+        return sample
+
+    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
+
+            # copy over dummy past residuals (must be done after set_timesteps)
+            dummy_past_residuals = [residual + 0.2, residual + 0.15, residual + 0.10]
+            scheduler.model_outputs = dummy_past_residuals[: scheduler.config.solver_order]
+
+            time_step_0 = scheduler.timesteps[5]
+            time_step_1 = scheduler.timesteps[6]
+
+            output_0 = scheduler.step(residual, time_step_0, sample, **kwargs).prev_sample
+            output_1 = scheduler.step(residual, time_step_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 [25, 50, 100, 999, 1000]:
+            self.check_over_configs(num_train_timesteps=timesteps)
+
+    def test_thresholding(self):
+        self.check_over_configs(thresholding=False)
+        for order in [1, 2, 3]:
+            for solver_type in ["midpoint", "heun"]:
+                for threshold in [0.5, 1.0, 2.0]:
+                    for prediction_type in ["epsilon", "sample"]:
+                        self.check_over_configs(
+                            thresholding=True,
+                            prediction_type=prediction_type,
+                            sample_max_value=threshold,
+                            algorithm_type="dpmsolver++",
+                            solver_order=order,
+                            solver_type=solver_type,
+                        )
+
+    def test_prediction_type(self):
+        for prediction_type in ["epsilon", "v_prediction"]:
+            self.check_over_configs(prediction_type=prediction_type)
+
+    def test_solver_order_and_type(self):
+        for algorithm_type in ["dpmsolver", "dpmsolver++"]:
+            for solver_type in ["midpoint", "heun"]:
+                for order in [1, 2, 3]:
+                    for prediction_type in ["epsilon", "sample"]:
+                        self.check_over_configs(
+                            solver_order=order,
+                            solver_type=solver_type,
+                            prediction_type=prediction_type,
+                            algorithm_type=algorithm_type,
+                        )
+                        sample = self.full_loop(
+                            solver_order=order,
+                            solver_type=solver_type,
+                            prediction_type=prediction_type,
+                            algorithm_type=algorithm_type,
+                        )
+                        assert not torch.isnan(sample).any(), "Samples have nan numbers"
+
+    def test_lower_order_final(self):
+        self.check_over_configs(lower_order_final=True)
+        self.check_over_configs(lower_order_final=False)
+
+    def test_inference_steps(self):
+        for num_inference_steps in [1, 2, 3, 5, 10, 50, 100, 999, 1000]:
+            self.check_over_forward(num_inference_steps=num_inference_steps, time_step=0)
+
+    def test_full_loop_no_noise(self):
+        sample = self.full_loop()
+        result_mean = torch.mean(torch.abs(sample))
+
+        assert abs(result_mean.item() - 0.3301) < 1e-3
+
+    def test_full_loop_no_noise_thres(self):
+        sample = self.full_loop(thresholding=True, dynamic_thresholding_ratio=0.87, sample_max_value=0.5)
+        result_mean = torch.mean(torch.abs(sample))
+
+        assert abs(result_mean.item() - 0.6405) < 1e-3
+
+    def test_full_loop_with_v_prediction(self):
+        sample = self.full_loop(prediction_type="v_prediction")
+        result_mean = torch.mean(torch.abs(sample))
+
+        assert abs(result_mean.item() - 0.2251) < 1e-3
+
+    def test_switch(self):
+        # make sure that iterating over schedulers with same config names gives same results
+        # for defaults
+        scheduler = DPMSolverMultistepScheduler(**self.get_scheduler_config())
+        sample = self.full_loop(scheduler=scheduler)
+        result_mean = torch.mean(torch.abs(sample))
+
+        assert abs(result_mean.item() - 0.3301) < 1e-3
+
+        scheduler = DPMSolverSinglestepScheduler.from_config(scheduler.config)
+        scheduler = UniPCMultistepScheduler.from_config(scheduler.config)
+        scheduler = DEISMultistepScheduler.from_config(scheduler.config)
+        scheduler = DPMSolverMultistepScheduler.from_config(scheduler.config)
+
+        sample = self.full_loop(scheduler=scheduler)
+        result_mean = torch.mean(torch.abs(sample))
+
+        assert abs(result_mean.item() - 0.3301) < 1e-3
+
+    def test_fp16_support(self):
+        scheduler_class = self.scheduler_classes[0]
+        scheduler_config = self.get_scheduler_config(thresholding=True, dynamic_thresholding_ratio=0)
+        scheduler = scheduler_class(**scheduler_config)
+
+        num_inference_steps = 10
+        model = self.dummy_model()
+        sample = self.dummy_sample_deter.half()
+        scheduler.set_timesteps(num_inference_steps)
+
+        for i, t in enumerate(scheduler.timesteps):
+            residual = model(sample, t)
+            sample = scheduler.step(residual, t, sample).prev_sample
+
+        assert sample.dtype == torch.float16

tests/schedulers/test_scheduler_dpm_single.py

+import tempfile
+
+import torch
+
+from diffusers import (
+    DEISMultistepScheduler,
+    DPMSolverMultistepScheduler,
+    DPMSolverSinglestepScheduler,
+    UniPCMultistepScheduler,
+)
+
+from .test_schedulers import SchedulerCommonTest
+
+
+class DPMSolverSinglestepSchedulerTest(SchedulerCommonTest):
+    scheduler_classes = (DPMSolverSinglestepScheduler,)
+    forward_default_kwargs = (("num_inference_steps", 25),)
+
+    def get_scheduler_config(self, **kwargs):
+        config = {
+            "num_train_timesteps": 1000,
+            "beta_start": 0.0001,
+            "beta_end": 0.02,
+            "beta_schedule": "linear",
+            "solver_order": 2,
+            "prediction_type": "epsilon",
+            "thresholding": False,
+            "sample_max_value": 1.0,
+            "algorithm_type": "dpmsolver++",
+            "solver_type": "midpoint",
+        }
+
+        config.update(**kwargs)
+        return config
+
+    def check_over_configs(self, time_step=0, **config):
+        kwargs = dict(self.forward_default_kwargs)
+        num_inference_steps = kwargs.pop("num_inference_steps", None)
+        sample = self.dummy_sample
+        residual = 0.1 * sample
+        dummy_past_residuals = [residual + 0.2, residual + 0.15, residual + 0.10]
+
+        for scheduler_class in self.scheduler_classes:
+            scheduler_config = self.get_scheduler_config(**config)
+            scheduler = scheduler_class(**scheduler_config)
+            scheduler.set_timesteps(num_inference_steps)
+            # copy over dummy past residuals
+            scheduler.model_outputs = dummy_past_residuals[: scheduler.config.solver_order]
+
+            with tempfile.TemporaryDirectory() as tmpdirname:
+                scheduler.save_config(tmpdirname)
+                new_scheduler = scheduler_class.from_pretrained(tmpdirname)
+                new_scheduler.set_timesteps(num_inference_steps)
+                # copy over dummy past residuals
+                new_scheduler.model_outputs = dummy_past_residuals[: new_scheduler.config.solver_order]
+
+            output, new_output = sample, sample
+            for t in range(time_step, time_step + scheduler.config.solver_order + 1):
+                output = scheduler.step(residual, t, output, **kwargs).prev_sample
+                new_output = new_scheduler.step(residual, t, new_output, **kwargs).prev_sample
+
+                assert torch.sum(torch.abs(output - new_output)) < 1e-5, "Scheduler outputs are not identical"
+
+    def test_from_save_pretrained(self):
+        pass
+
+    def check_over_forward(self, time_step=0, **forward_kwargs):
+        kwargs = dict(self.forward_default_kwargs)
+        num_inference_steps = kwargs.pop("num_inference_steps", None)
+        sample = self.dummy_sample
+        residual = 0.1 * sample
+        dummy_past_residuals = [residual + 0.2, residual + 0.15, residual + 0.10]
+
+        for scheduler_class in self.scheduler_classes:
+            scheduler_config = self.get_scheduler_config()
+            scheduler = scheduler_class(**scheduler_config)
+            scheduler.set_timesteps(num_inference_steps)
+
+            # copy over dummy past residuals (must be after setting timesteps)
+            scheduler.model_outputs = dummy_past_residuals[: scheduler.config.solver_order]
+
+            with tempfile.TemporaryDirectory() as tmpdirname:
+                scheduler.save_config(tmpdirname)
+                new_scheduler = scheduler_class.from_pretrained(tmpdirname)
+                # copy over dummy past residuals
+                new_scheduler.set_timesteps(num_inference_steps)
+
+                # copy over dummy past residual (must be after setting timesteps)
+                new_scheduler.model_outputs = dummy_past_residuals[: new_scheduler.config.solver_order]
+
+            output = scheduler.step(residual, time_step, sample, **kwargs).prev_sample
+            new_output = new_scheduler.step(residual, time_step, sample, **kwargs).prev_sample
+
+            assert torch.sum(torch.abs(output - new_output)) < 1e-5, "Scheduler outputs are not identical"
+
+    def full_loop(self, scheduler=None, **config):
+        if scheduler is None:
+            scheduler_class = self.scheduler_classes[0]
+            scheduler_config = self.get_scheduler_config(**config)
+            scheduler = scheduler_class(**scheduler_config)
+
+        scheduler_class = self.scheduler_classes[0]
+        scheduler_config = self.get_scheduler_config(**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.timesteps):
+            residual = model(sample, t)
+            sample = scheduler.step(residual, t, sample).prev_sample
+
+        return sample
+
+    def test_timesteps(self):
+        for timesteps in [25, 50, 100, 999, 1000]:
+            self.check_over_configs(num_train_timesteps=timesteps)
+
+    def test_switch(self):
+        # make sure that iterating over schedulers with same config names gives same results
+        # for defaults
+        scheduler = DPMSolverSinglestepScheduler(**self.get_scheduler_config())
+        sample = self.full_loop(scheduler=scheduler)
+        result_mean = torch.mean(torch.abs(sample))
+
+        assert abs(result_mean.item() - 0.2791) < 1e-3
+
+        scheduler = DEISMultistepScheduler.from_config(scheduler.config)
+        scheduler = DPMSolverMultistepScheduler.from_config(scheduler.config)
+        scheduler = UniPCMultistepScheduler.from_config(scheduler.config)
+        scheduler = DPMSolverSinglestepScheduler.from_config(scheduler.config)
+
+        sample = self.full_loop(scheduler=scheduler)
+        result_mean = torch.mean(torch.abs(sample))
+
+        assert abs(result_mean.item() - 0.2791) < 1e-3
+
+    def test_thresholding(self):
+        self.check_over_configs(thresholding=False)
+        for order in [1, 2, 3]:
+            for solver_type in ["midpoint", "heun"]:
+                for threshold in [0.5, 1.0, 2.0]:
+                    for prediction_type in ["epsilon", "sample"]:
+                        self.check_over_configs(
+                            thresholding=True,
+                            prediction_type=prediction_type,
+                            sample_max_value=threshold,
+                            algorithm_type="dpmsolver++",
+                            solver_order=order,
+                            solver_type=solver_type,
+                        )
+
+    def test_prediction_type(self):
+        for prediction_type in ["epsilon", "v_prediction"]:
+            self.check_over_configs(prediction_type=prediction_type)
+
+    def test_solver_order_and_type(self):
+        for algorithm_type in ["dpmsolver", "dpmsolver++"]:
+            for solver_type in ["midpoint", "heun"]:
+                for order in [1, 2, 3]:
+                    for prediction_type in ["epsilon", "sample"]:
+                        self.check_over_configs(
+                            solver_order=order,
+                            solver_type=solver_type,
+                            prediction_type=prediction_type,
+                            algorithm_type=algorithm_type,
+                        )
+                        sample = self.full_loop(
+                            solver_order=order,
+                            solver_type=solver_type,
+                            prediction_type=prediction_type,
+                            algorithm_type=algorithm_type,
+                        )
+                        assert not torch.isnan(sample).any(), "Samples have nan numbers"
+
+    def test_lower_order_final(self):
+        self.check_over_configs(lower_order_final=True)
+        self.check_over_configs(lower_order_final=False)
+
+    def test_inference_steps(self):
+        for num_inference_steps in [1, 2, 3, 5, 10, 50, 100, 999, 1000]:
+            self.check_over_forward(num_inference_steps=num_inference_steps, time_step=0)
+
+    def test_full_loop_no_noise(self):
+        sample = self.full_loop()
+        result_mean = torch.mean(torch.abs(sample))
+
+        assert abs(result_mean.item() - 0.2791) < 1e-3
+
+    def test_full_loop_with_v_prediction(self):
+        sample = self.full_loop(prediction_type="v_prediction")
+        result_mean = torch.mean(torch.abs(sample))
+
+        assert abs(result_mean.item() - 0.1453) < 1e-3
+
+    def test_fp16_support(self):
+        scheduler_class = self.scheduler_classes[0]
+        scheduler_config = self.get_scheduler_config(thresholding=True, dynamic_thresholding_ratio=0)
+        scheduler = scheduler_class(**scheduler_config)
+
+        num_inference_steps = 10
+        model = self.dummy_model()
+        sample = self.dummy_sample_deter.half()
+        scheduler.set_timesteps(num_inference_steps)
+
+        for i, t in enumerate(scheduler.timesteps):
+            residual = model(sample, t)
+            sample = scheduler.step(residual, t, sample).prev_sample
+
+        assert sample.dtype == torch.float16

tests/schedulers/test_scheduler_euler.py

+import torch
+
+from diffusers import EulerDiscreteScheduler
+from diffusers.utils import torch_device
+
+from .test_schedulers import SchedulerCommonTest
+
+
+class EulerDiscreteSchedulerTest(SchedulerCommonTest):
+    scheduler_classes = (EulerDiscreteScheduler,)
+    num_inference_steps = 10
+
+    def get_scheduler_config(self, **kwargs):
+        config = {
+            "num_train_timesteps": 1100,
+            "beta_start": 0.0001,
+            "beta_end": 0.02,
+            "beta_schedule": "linear",
+        }
+
+        config.update(**kwargs)
+        return config
+
+    def test_timesteps(self):
+        for timesteps in [10, 50, 100, 1000]:
+            self.check_over_configs(num_train_timesteps=timesteps)
+
+    def test_betas(self):
+        for beta_start, beta_end in zip([0.00001, 0.0001, 0.001], [0.0002, 0.002, 0.02]):
+            self.check_over_configs(beta_start=beta_start, beta_end=beta_end)
+
+    def test_schedules(self):
+        for schedule in ["linear", "scaled_linear"]:
+            self.check_over_configs(beta_schedule=schedule)
+
+    def test_prediction_type(self):
+        for prediction_type in ["epsilon", "v_prediction"]:
+            self.check_over_configs(prediction_type=prediction_type)
+
+    def test_full_loop_no_noise(self):
+        scheduler_class = self.scheduler_classes[0]
+        scheduler_config = self.get_scheduler_config()
+        scheduler = scheduler_class(**scheduler_config)
+
+        scheduler.set_timesteps(self.num_inference_steps)
+
+        generator = torch.manual_seed(0)
+
+        model = self.dummy_model()
+        sample = self.dummy_sample_deter * scheduler.init_noise_sigma
+        sample = sample.to(torch_device)
+
+        for i, t in enumerate(scheduler.timesteps):
+            sample = scheduler.scale_model_input(sample, t)
+
+            model_output = model(sample, t)
+
+            output = scheduler.step(model_output, t, sample, generator=generator)
+            sample = output.prev_sample
+
+        result_sum = torch.sum(torch.abs(sample))
+        result_mean = torch.mean(torch.abs(sample))
+
+        assert abs(result_sum.item() - 10.0807) < 1e-2
+        assert abs(result_mean.item() - 0.0131) < 1e-3
+
+    def test_full_loop_with_v_prediction(self):
+        scheduler_class = self.scheduler_classes[0]
+        scheduler_config = self.get_scheduler_config(prediction_type="v_prediction")
+        scheduler = scheduler_class(**scheduler_config)
+
+        scheduler.set_timesteps(self.num_inference_steps)
+
+        generator = torch.manual_seed(0)
+
+        model = self.dummy_model()
+        sample = self.dummy_sample_deter * scheduler.init_noise_sigma
+        sample = sample.to(torch_device)
+
+        for i, t in enumerate(scheduler.timesteps):
+            sample = scheduler.scale_model_input(sample, t)
+
+            model_output = model(sample, t)
+
+            output = scheduler.step(model_output, t, sample, generator=generator)
+            sample = output.prev_sample
+
+        result_sum = torch.sum(torch.abs(sample))
+        result_mean = torch.mean(torch.abs(sample))
+
+        assert abs(result_sum.item() - 0.0002) < 1e-2
+        assert abs(result_mean.item() - 2.2676e-06) < 1e-3
+
+    def test_full_loop_device(self):
+        scheduler_class = self.scheduler_classes[0]
+        scheduler_config = self.get_scheduler_config()
+        scheduler = scheduler_class(**scheduler_config)
+
+        scheduler.set_timesteps(self.num_inference_steps, device=torch_device)
+
+        generator = torch.manual_seed(0)
+
+        model = self.dummy_model()
+        sample = self.dummy_sample_deter * scheduler.init_noise_sigma
+        sample = sample.to(torch_device)
+
+        for t in scheduler.timesteps:
+            sample = scheduler.scale_model_input(sample, t)
+
+            model_output = model(sample, t)
+
+            output = scheduler.step(model_output, t, sample, generator=generator)
+            sample = output.prev_sample
+
+        result_sum = torch.sum(torch.abs(sample))
+        result_mean = torch.mean(torch.abs(sample))
+
+        assert abs(result_sum.item() - 10.0807) < 1e-2
+        assert abs(result_mean.item() - 0.0131) < 1e-3

tests/schedulers/test_scheduler_euler_ancestral.py

+import torch
+
+from diffusers import EulerAncestralDiscreteScheduler
+from diffusers.utils import torch_device
+
+from .test_schedulers import SchedulerCommonTest
+
+
+class EulerAncestralDiscreteSchedulerTest(SchedulerCommonTest):
+    scheduler_classes = (EulerAncestralDiscreteScheduler,)
+    num_inference_steps = 10
+
+    def get_scheduler_config(self, **kwargs):
+        config = {
+            "num_train_timesteps": 1100,
+            "beta_start": 0.0001,
+            "beta_end": 0.02,
+            "beta_schedule": "linear",
+        }
+
+        config.update(**kwargs)
+        return config
+
+    def test_timesteps(self):
+        for timesteps in [10, 50, 100, 1000]:
+            self.check_over_configs(num_train_timesteps=timesteps)
+
+    def test_betas(self):
+        for beta_start, beta_end in zip([0.00001, 0.0001, 0.001], [0.0002, 0.002, 0.02]):
+            self.check_over_configs(beta_start=beta_start, beta_end=beta_end)
+
+    def test_schedules(self):
+        for schedule in ["linear", "scaled_linear"]:
+            self.check_over_configs(beta_schedule=schedule)
+
+    def test_prediction_type(self):
+        for prediction_type in ["epsilon", "v_prediction"]:
+            self.check_over_configs(prediction_type=prediction_type)
+
+    def test_full_loop_no_noise(self):
+        scheduler_class = self.scheduler_classes[0]
+        scheduler_config = self.get_scheduler_config()
+        scheduler = scheduler_class(**scheduler_config)
+
+        scheduler.set_timesteps(self.num_inference_steps)
+
+        generator = torch.manual_seed(0)
+
+        model = self.dummy_model()
+        sample = self.dummy_sample_deter * scheduler.init_noise_sigma
+        sample = sample.to(torch_device)
+
+        for i, t in enumerate(scheduler.timesteps):
+            sample = scheduler.scale_model_input(sample, t)
+
+            model_output = model(sample, t)
+
+            output = scheduler.step(model_output, t, sample, generator=generator)
+            sample = output.prev_sample
+
+        result_sum = torch.sum(torch.abs(sample))
+        result_mean = torch.mean(torch.abs(sample))
+
+        assert abs(result_sum.item() - 152.3192) < 1e-2
+        assert abs(result_mean.item() - 0.1983) < 1e-3
+
+    def test_full_loop_with_v_prediction(self):
+        scheduler_class = self.scheduler_classes[0]
+        scheduler_config = self.get_scheduler_config(prediction_type="v_prediction")
+        scheduler = scheduler_class(**scheduler_config)
+
+        scheduler.set_timesteps(self.num_inference_steps)
+
+        generator = torch.manual_seed(0)
+
+        model = self.dummy_model()
+        sample = self.dummy_sample_deter * scheduler.init_noise_sigma
+        sample = sample.to(torch_device)
+
+        for i, t in enumerate(scheduler.timesteps):
+            sample = scheduler.scale_model_input(sample, t)
+
+            model_output = model(sample, t)
+
+            output = scheduler.step(model_output, t, sample, generator=generator)
+            sample = output.prev_sample
+
+        result_sum = torch.sum(torch.abs(sample))
+        result_mean = torch.mean(torch.abs(sample))
+
+        assert abs(result_sum.item() - 108.4439) < 1e-2
+        assert abs(result_mean.item() - 0.1412) < 1e-3
+
+    def test_full_loop_device(self):
+        scheduler_class = self.scheduler_classes[0]
+        scheduler_config = self.get_scheduler_config()
+        scheduler = scheduler_class(**scheduler_config)
+
+        scheduler.set_timesteps(self.num_inference_steps, device=torch_device)
+        generator = torch.manual_seed(0)
+
+        model = self.dummy_model()
+        sample = self.dummy_sample_deter * scheduler.init_noise_sigma
+        sample = sample.to(torch_device)
+
+        for t in scheduler.timesteps:
+            sample = scheduler.scale_model_input(sample, t)
+
+            model_output = model(sample, t)
+
+            output = scheduler.step(model_output, t, sample, generator=generator)
+            sample = output.prev_sample
+
+        result_sum = torch.sum(torch.abs(sample))
+        result_mean = torch.mean(torch.abs(sample))
+
+        assert abs(result_sum.item() - 152.3192) < 1e-2
+        assert abs(result_mean.item() - 0.1983) < 1e-3

tests/schedulers/test_scheduler_heun.py

+import torch
+
+from diffusers import HeunDiscreteScheduler
+from diffusers.utils import torch_device
+
+from .test_schedulers import SchedulerCommonTest
+
+
+class HeunDiscreteSchedulerTest(SchedulerCommonTest):
+    scheduler_classes = (HeunDiscreteScheduler,)
+    num_inference_steps = 10
+
+    def get_scheduler_config(self, **kwargs):
+        config = {
+            "num_train_timesteps": 1100,
+            "beta_start": 0.0001,
+            "beta_end": 0.02,
+            "beta_schedule": "linear",
+        }
+
+        config.update(**kwargs)
+        return config
+
+    def test_timesteps(self):
+        for timesteps in [10, 50, 100, 1000]:
+            self.check_over_configs(num_train_timesteps=timesteps)
+
+    def test_betas(self):
+        for beta_start, beta_end in zip([0.00001, 0.0001, 0.001], [0.0002, 0.002, 0.02]):
+            self.check_over_configs(beta_start=beta_start, beta_end=beta_end)
+
+    def test_schedules(self):
+        for schedule in ["linear", "scaled_linear"]:
+            self.check_over_configs(beta_schedule=schedule)
+
+    def test_prediction_type(self):
+        for prediction_type in ["epsilon", "v_prediction"]:
+            self.check_over_configs(prediction_type=prediction_type)
+
+    def test_full_loop_no_noise(self):
+        scheduler_class = self.scheduler_classes[0]
+        scheduler_config = self.get_scheduler_config()
+        scheduler = scheduler_class(**scheduler_config)
+
+        scheduler.set_timesteps(self.num_inference_steps)
+
+        model = self.dummy_model()
+        sample = self.dummy_sample_deter * scheduler.init_noise_sigma
+        sample = sample.to(torch_device)
+
+        for i, t in enumerate(scheduler.timesteps):
+            sample = scheduler.scale_model_input(sample, t)
+
+            model_output = model(sample, t)
+
+            output = scheduler.step(model_output, t, sample)
+            sample = output.prev_sample
+
+        result_sum = torch.sum(torch.abs(sample))
+        result_mean = torch.mean(torch.abs(sample))
+
+        if torch_device in ["cpu", "mps"]:
+            assert abs(result_sum.item() - 0.1233) < 1e-2
+            assert abs(result_mean.item() - 0.0002) < 1e-3
+        else:
+            # CUDA
+            assert abs(result_sum.item() - 0.1233) < 1e-2
+            assert abs(result_mean.item() - 0.0002) < 1e-3
+
+    def test_full_loop_with_v_prediction(self):
+        scheduler_class = self.scheduler_classes[0]
+        scheduler_config = self.get_scheduler_config(prediction_type="v_prediction")
+        scheduler = scheduler_class(**scheduler_config)
+
+        scheduler.set_timesteps(self.num_inference_steps)
+
+        model = self.dummy_model()
+        sample = self.dummy_sample_deter * scheduler.init_noise_sigma
+        sample = sample.to(torch_device)
+
+        for i, t in enumerate(scheduler.timesteps):
+            sample = scheduler.scale_model_input(sample, t)
+
+            model_output = model(sample, t)
+
+            output = scheduler.step(model_output, t, sample)
+            sample = output.prev_sample
+
+        result_sum = torch.sum(torch.abs(sample))
+        result_mean = torch.mean(torch.abs(sample))
+
+        if torch_device in ["cpu", "mps"]:
+            assert abs(result_sum.item() - 4.6934e-07) < 1e-2
+            assert abs(result_mean.item() - 6.1112e-10) < 1e-3
+        else:
+            # CUDA
+            assert abs(result_sum.item() - 4.693428650170972e-07) < 1e-2
+            assert abs(result_mean.item() - 0.0002) < 1e-3
+
+    def test_full_loop_device(self):
+        scheduler_class = self.scheduler_classes[0]
+        scheduler_config = self.get_scheduler_config()
+        scheduler = scheduler_class(**scheduler_config)
+
+        scheduler.set_timesteps(self.num_inference_steps, device=torch_device)
+
+        model = self.dummy_model()
+        sample = self.dummy_sample_deter.to(torch_device) * scheduler.init_noise_sigma
+
+        for t in scheduler.timesteps:
+            sample = scheduler.scale_model_input(sample, t)
+
+            model_output = model(sample, t)
+
+            output = scheduler.step(model_output, t, sample)
+            sample = output.prev_sample
+
+        result_sum = torch.sum(torch.abs(sample))
+        result_mean = torch.mean(torch.abs(sample))
+
+        if str(torch_device).startswith("cpu"):
+            # The following sum varies between 148 and 156 on mps. Why?
+            assert abs(result_sum.item() - 0.1233) < 1e-2
+            assert abs(result_mean.item() - 0.0002) < 1e-3
+        elif str(torch_device).startswith("mps"):
+            # Larger tolerance on mps
+            assert abs(result_mean.item() - 0.0002) < 1e-2
+        else:
+            # CUDA
+            assert abs(result_sum.item() - 0.1233) < 1e-2
+            assert abs(result_mean.item() - 0.0002) < 1e-3

tests/schedulers/test_scheduler_ipndm.py

+import tempfile
+
+import torch
+
+from diffusers import IPNDMScheduler
+
+from .test_schedulers import SchedulerCommonTest
+
+
+class IPNDMSchedulerTest(SchedulerCommonTest):
+    scheduler_classes = (IPNDMScheduler,)
+    forward_default_kwargs = (("num_inference_steps", 50),)
+
+    def get_scheduler_config(self, **kwargs):
+        config = {"num_train_timesteps": 1000}
+        config.update(**kwargs)
+        return config
+
+    def check_over_configs(self, time_step=0, **config):
+        kwargs = dict(self.forward_default_kwargs)
+        num_inference_steps = kwargs.pop("num_inference_steps", None)
+        sample = self.dummy_sample
+        residual = 0.1 * sample
+        dummy_past_residuals = [residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]
+
+        for scheduler_class in self.scheduler_classes:
+            scheduler_config = self.get_scheduler_config(**config)
+            scheduler = scheduler_class(**scheduler_config)
+            scheduler.set_timesteps(num_inference_steps)
+            # copy over dummy past residuals
+            scheduler.ets = dummy_past_residuals[:]
+
+            if time_step is None:
+                time_step = scheduler.timesteps[len(scheduler.timesteps) // 2]
+
+            with tempfile.TemporaryDirectory() as tmpdirname:
+                scheduler.save_config(tmpdirname)
+                new_scheduler = scheduler_class.from_pretrained(tmpdirname)
+                new_scheduler.set_timesteps(num_inference_steps)
+                # copy over dummy past residuals
+                new_scheduler.ets = dummy_past_residuals[:]
+
+            output = scheduler.step(residual, time_step, sample, **kwargs).prev_sample
+            new_output = new_scheduler.step(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(residual, time_step, sample, **kwargs).prev_sample
+            new_output = new_scheduler.step(residual, time_step, sample, **kwargs).prev_sample
+
+            assert torch.sum(torch.abs(output - new_output)) < 1e-5, "Scheduler outputs are not identical"
+
+    def test_from_save_pretrained(self):
+        pass
+
+    def check_over_forward(self, time_step=0, **forward_kwargs):
+        kwargs = dict(self.forward_default_kwargs)
+        num_inference_steps = kwargs.pop("num_inference_steps", None)
+        sample = self.dummy_sample
+        residual = 0.1 * sample
+        dummy_past_residuals = [residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]
+
+        for scheduler_class in self.scheduler_classes:
+            scheduler_config = self.get_scheduler_config()
+            scheduler = scheduler_class(**scheduler_config)
+            scheduler.set_timesteps(num_inference_steps)
+
+            # copy over dummy past residuals (must be after setting timesteps)
+            scheduler.ets = dummy_past_residuals[:]
+
+            if time_step is None:
+                time_step = scheduler.timesteps[len(scheduler.timesteps) // 2]
+
+            with tempfile.TemporaryDirectory() as tmpdirname:
+                scheduler.save_config(tmpdirname)
+                new_scheduler = scheduler_class.from_pretrained(tmpdirname)
+                # copy over dummy past residuals
+                new_scheduler.set_timesteps(num_inference_steps)
+
+                # copy over dummy past residual (must be after setting timesteps)
+                new_scheduler.ets = dummy_past_residuals[:]
+
+            output = scheduler.step(residual, time_step, sample, **kwargs).prev_sample
+            new_output = new_scheduler.step(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(residual, time_step, sample, **kwargs).prev_sample
+            new_output = new_scheduler.step(residual, time_step, sample, **kwargs).prev_sample
+
+            assert torch.sum(torch.abs(output - new_output)) < 1e-5, "Scheduler outputs are not identical"
+
+    def full_loop(self, **config):
+        scheduler_class = self.scheduler_classes[0]
+        scheduler_config = self.get_scheduler_config(**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.timesteps):
+            residual = model(sample, t)
+            sample = scheduler.step(residual, t, sample).prev_sample
+
+        for i, t in enumerate(scheduler.timesteps):
+            residual = model(sample, t)
+            sample = scheduler.step(residual, t, sample).prev_sample
+
+        return sample
+
+    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
+
+            # copy over dummy past residuals (must be done after set_timesteps)
+            dummy_past_residuals = [residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]
+            scheduler.ets = dummy_past_residuals[:]
+
+            time_step_0 = scheduler.timesteps[5]
+            time_step_1 = scheduler.timesteps[6]
+
+            output_0 = scheduler.step(residual, time_step_0, sample, **kwargs).prev_sample
+            output_1 = scheduler.step(residual, time_step_1, sample, **kwargs).prev_sample
+
+            self.assertEqual(output_0.shape, sample.shape)
+            self.assertEqual(output_0.shape, output_1.shape)
+
+            output_0 = scheduler.step(residual, time_step_0, sample, **kwargs).prev_sample
+            output_1 = scheduler.step(residual, time_step_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, time_step=None)
+
+    def test_inference_steps(self):
+        for t, num_inference_steps in zip([1, 5, 10], [10, 50, 100]):
+            self.check_over_forward(num_inference_steps=num_inference_steps, time_step=None)
+
+    def test_full_loop_no_noise(self):
+        sample = self.full_loop()
+        result_mean = torch.mean(torch.abs(sample))
+
+        assert abs(result_mean.item() - 2540529) < 10

tests/schedulers/test_scheduler_kdpm2_ancestral.py

+import torch
+
+from diffusers import KDPM2AncestralDiscreteScheduler
+from diffusers.utils import torch_device
+
+from .test_schedulers import SchedulerCommonTest
+
+
+class KDPM2AncestralDiscreteSchedulerTest(SchedulerCommonTest):
+    scheduler_classes = (KDPM2AncestralDiscreteScheduler,)
+    num_inference_steps = 10
+
+    def get_scheduler_config(self, **kwargs):
+        config = {
+            "num_train_timesteps": 1100,
+            "beta_start": 0.0001,
+            "beta_end": 0.02,
+            "beta_schedule": "linear",
+        }
+
+        config.update(**kwargs)
+        return config
+
+    def test_timesteps(self):
+        for timesteps in [10, 50, 100, 1000]:
+            self.check_over_configs(num_train_timesteps=timesteps)
+
+    def test_betas(self):
+        for beta_start, beta_end in zip([0.00001, 0.0001, 0.001], [0.0002, 0.002, 0.02]):
+            self.check_over_configs(beta_start=beta_start, beta_end=beta_end)
+
+    def test_schedules(self):
+        for schedule in ["linear", "scaled_linear"]:
+            self.check_over_configs(beta_schedule=schedule)
+
+    def test_full_loop_no_noise(self):
+        if torch_device == "mps":
+            return
+        scheduler_class = self.scheduler_classes[0]
+        scheduler_config = self.get_scheduler_config()
+        scheduler = scheduler_class(**scheduler_config)
+
+        scheduler.set_timesteps(self.num_inference_steps)
+
+        generator = torch.manual_seed(0)
+
+        model = self.dummy_model()
+        sample = self.dummy_sample_deter * scheduler.init_noise_sigma
+        sample = sample.to(torch_device)
+
+        for i, t in enumerate(scheduler.timesteps):
+            sample = scheduler.scale_model_input(sample, t)
+
+            model_output = model(sample, t)
+
+            output = scheduler.step(model_output, t, sample, generator=generator)
+            sample = output.prev_sample
+
+        result_sum = torch.sum(torch.abs(sample))
+        result_mean = torch.mean(torch.abs(sample))
+
+        assert abs(result_sum.item() - 13849.3877) < 1e-2
+        assert abs(result_mean.item() - 18.0331) < 5e-3
+
+    def test_prediction_type(self):
+        for prediction_type in ["epsilon", "v_prediction"]:
+            self.check_over_configs(prediction_type=prediction_type)
+
+    def test_full_loop_with_v_prediction(self):
+        if torch_device == "mps":
+            return
+        scheduler_class = self.scheduler_classes[0]
+        scheduler_config = self.get_scheduler_config(prediction_type="v_prediction")
+        scheduler = scheduler_class(**scheduler_config)
+
+        scheduler.set_timesteps(self.num_inference_steps)
+
+        model = self.dummy_model()
+        sample = self.dummy_sample_deter * scheduler.init_noise_sigma
+        sample = sample.to(torch_device)
+
+        generator = torch.manual_seed(0)
+
+        for i, t in enumerate(scheduler.timesteps):
+            sample = scheduler.scale_model_input(sample, t)
+
+            model_output = model(sample, t)
+
+            output = scheduler.step(model_output, t, sample, generator=generator)
+            sample = output.prev_sample
+
+        result_sum = torch.sum(torch.abs(sample))
+        result_mean = torch.mean(torch.abs(sample))
+
+        assert abs(result_sum.item() - 328.9970) < 1e-2
+        assert abs(result_mean.item() - 0.4284) < 1e-3
+
+    def test_full_loop_device(self):
+        if torch_device == "mps":
+            return
+        scheduler_class = self.scheduler_classes[0]
+        scheduler_config = self.get_scheduler_config()
+        scheduler = scheduler_class(**scheduler_config)
+
+        scheduler.set_timesteps(self.num_inference_steps, device=torch_device)
+        generator = torch.manual_seed(0)
+
+        model = self.dummy_model()
+        sample = self.dummy_sample_deter.to(torch_device) * scheduler.init_noise_sigma
+
+        for t in scheduler.timesteps:
+            sample = scheduler.scale_model_input(sample, t)
+
+            model_output = model(sample, t)
+
+            output = scheduler.step(model_output, t, sample, generator=generator)
+            sample = output.prev_sample
+
+        result_sum = torch.sum(torch.abs(sample))
+        result_mean = torch.mean(torch.abs(sample))
+
+        assert abs(result_sum.item() - 13849.3818) < 1e-1
+        assert abs(result_mean.item() - 18.0331) < 1e-3

tests/schedulers/test_scheduler_kdpm2_discrete.py

+import torch
+
+from diffusers import KDPM2DiscreteScheduler
+from diffusers.utils import torch_device
+
+from .test_schedulers import SchedulerCommonTest
+
+
+class KDPM2DiscreteSchedulerTest(SchedulerCommonTest):
+    scheduler_classes = (KDPM2DiscreteScheduler,)
+    num_inference_steps = 10
+
+    def get_scheduler_config(self, **kwargs):
+        config = {
+            "num_train_timesteps": 1100,
+            "beta_start": 0.0001,
+            "beta_end": 0.02,
+            "beta_schedule": "linear",
+        }
+
+        config.update(**kwargs)
+        return config
+
+    def test_timesteps(self):
+        for timesteps in [10, 50, 100, 1000]:
+            self.check_over_configs(num_train_timesteps=timesteps)
+
+    def test_betas(self):
+        for beta_start, beta_end in zip([0.00001, 0.0001, 0.001], [0.0002, 0.002, 0.02]):
+            self.check_over_configs(beta_start=beta_start, beta_end=beta_end)
+
+    def test_schedules(self):
+        for schedule in ["linear", "scaled_linear"]:
+            self.check_over_configs(beta_schedule=schedule)
+
+    def test_prediction_type(self):
+        for prediction_type in ["epsilon", "v_prediction"]:
+            self.check_over_configs(prediction_type=prediction_type)
+
+    def test_full_loop_with_v_prediction(self):
+        scheduler_class = self.scheduler_classes[0]
+        scheduler_config = self.get_scheduler_config(prediction_type="v_prediction")
+        scheduler = scheduler_class(**scheduler_config)
+
+        scheduler.set_timesteps(self.num_inference_steps)
+
+        model = self.dummy_model()
+        sample = self.dummy_sample_deter * scheduler.init_noise_sigma
+        sample = sample.to(torch_device)
+
+        for i, t in enumerate(scheduler.timesteps):
+            sample = scheduler.scale_model_input(sample, t)
+
+            model_output = model(sample, t)
+
+            output = scheduler.step(model_output, t, sample)
+            sample = output.prev_sample
+
+        result_sum = torch.sum(torch.abs(sample))
+        result_mean = torch.mean(torch.abs(sample))
+
+        if torch_device in ["cpu", "mps"]:
+            assert abs(result_sum.item() - 4.6934e-07) < 1e-2
+            assert abs(result_mean.item() - 6.1112e-10) < 1e-3
+        else:
+            # CUDA
+            assert abs(result_sum.item() - 4.693428650170972e-07) < 1e-2
+            assert abs(result_mean.item() - 0.0002) < 1e-3
+
+    def test_full_loop_no_noise(self):
+        if torch_device == "mps":
+            return
+        scheduler_class = self.scheduler_classes[0]
+        scheduler_config = self.get_scheduler_config()
+        scheduler = scheduler_class(**scheduler_config)
+
+        scheduler.set_timesteps(self.num_inference_steps)
+
+        model = self.dummy_model()
+        sample = self.dummy_sample_deter * scheduler.init_noise_sigma
+        sample = sample.to(torch_device)
+
+        for i, t in enumerate(scheduler.timesteps):
+            sample = scheduler.scale_model_input(sample, t)
+
+            model_output = model(sample, t)
+
+            output = scheduler.step(model_output, t, sample)
+            sample = output.prev_sample
+
+        result_sum = torch.sum(torch.abs(sample))
+        result_mean = torch.mean(torch.abs(sample))
+
+        if torch_device in ["cpu", "mps"]:
+            assert abs(result_sum.item() - 20.4125) < 1e-2
+            assert abs(result_mean.item() - 0.0266) < 1e-3
+        else:
+            # CUDA
+            assert abs(result_sum.item() - 20.4125) < 1e-2
+            assert abs(result_mean.item() - 0.0266) < 1e-3
+
+    def test_full_loop_device(self):
+        if torch_device == "mps":
+            return
+        scheduler_class = self.scheduler_classes[0]
+        scheduler_config = self.get_scheduler_config()
+        scheduler = scheduler_class(**scheduler_config)
+
+        scheduler.set_timesteps(self.num_inference_steps, device=torch_device)
+
+        model = self.dummy_model()
+        sample = self.dummy_sample_deter.to(torch_device) * scheduler.init_noise_sigma
+
+        for t in scheduler.timesteps:
+            sample = scheduler.scale_model_input(sample, t)
+
+            model_output = model(sample, t)
+
+            output = scheduler.step(model_output, t, sample)
+            sample = output.prev_sample
+
+        result_sum = torch.sum(torch.abs(sample))
+        result_mean = torch.mean(torch.abs(sample))
+
+        if str(torch_device).startswith("cpu"):
+            # The following sum varies between 148 and 156 on mps. Why?
+            assert abs(result_sum.item() - 20.4125) < 1e-2
+            assert abs(result_mean.item() - 0.0266) < 1e-3
+        else:
+            # CUDA
+            assert abs(result_sum.item() - 20.4125) < 1e-2
+            assert abs(result_mean.item() - 0.0266) < 1e-3

tests/schedulers/test_scheduler_lms.py

+import torch
+
+from diffusers import LMSDiscreteScheduler
+from diffusers.utils import torch_device
+
+from .test_schedulers import SchedulerCommonTest
+
+
+class LMSDiscreteSchedulerTest(SchedulerCommonTest):
+    scheduler_classes = (LMSDiscreteScheduler,)
+    num_inference_steps = 10
+
+    def get_scheduler_config(self, **kwargs):
+        config = {
+            "num_train_timesteps": 1100,
+            "beta_start": 0.0001,
+            "beta_end": 0.02,
+            "beta_schedule": "linear",
+        }
+
+        config.update(**kwargs)
+        return config
+
+    def test_timesteps(self):
+        for timesteps in [10, 50, 100, 1000]:
+            self.check_over_configs(num_train_timesteps=timesteps)
+
+    def test_betas(self):
+        for beta_start, beta_end in zip([0.00001, 0.0001, 0.001], [0.0002, 0.002, 0.02]):
+            self.check_over_configs(beta_start=beta_start, beta_end=beta_end)
+
+    def test_schedules(self):
+        for schedule in ["linear", "scaled_linear"]:
+            self.check_over_configs(beta_schedule=schedule)
+
+    def test_prediction_type(self):
+        for prediction_type in ["epsilon", "v_prediction"]:
+            self.check_over_configs(prediction_type=prediction_type)
+
+    def test_time_indices(self):
+        for t in [0, 500, 800]:
+            self.check_over_forward(time_step=t)
+
+    def test_full_loop_no_noise(self):
+        scheduler_class = self.scheduler_classes[0]
+        scheduler_config = self.get_scheduler_config()
+        scheduler = scheduler_class(**scheduler_config)
+
+        scheduler.set_timesteps(self.num_inference_steps)
+
+        model = self.dummy_model()
+        sample = self.dummy_sample_deter * scheduler.init_noise_sigma
+
+        for i, t in enumerate(scheduler.timesteps):
+            sample = scheduler.scale_model_input(sample, t)
+
+            model_output = model(sample, t)
+
+            output = scheduler.step(model_output, t, sample)
+            sample = output.prev_sample
+
+        result_sum = torch.sum(torch.abs(sample))
+        result_mean = torch.mean(torch.abs(sample))
+
+        assert abs(result_sum.item() - 1006.388) < 1e-2
+        assert abs(result_mean.item() - 1.31) < 1e-3
+
+    def test_full_loop_with_v_prediction(self):
+        scheduler_class = self.scheduler_classes[0]
+        scheduler_config = self.get_scheduler_config(prediction_type="v_prediction")
+        scheduler = scheduler_class(**scheduler_config)
+
+        scheduler.set_timesteps(self.num_inference_steps)
+
+        model = self.dummy_model()
+        sample = self.dummy_sample_deter * scheduler.init_noise_sigma
+
+        for i, t in enumerate(scheduler.timesteps):
+            sample = scheduler.scale_model_input(sample, t)
+
+            model_output = model(sample, t)
+
+            output = scheduler.step(model_output, t, sample)
+            sample = output.prev_sample
+
+        result_sum = torch.sum(torch.abs(sample))
+        result_mean = torch.mean(torch.abs(sample))
+
+        assert abs(result_sum.item() - 0.0017) < 1e-2
+        assert abs(result_mean.item() - 2.2676e-06) < 1e-3
+
+    def test_full_loop_device(self):
+        scheduler_class = self.scheduler_classes[0]
+        scheduler_config = self.get_scheduler_config()
+        scheduler = scheduler_class(**scheduler_config)
+
+        scheduler.set_timesteps(self.num_inference_steps, device=torch_device)
+
+        model = self.dummy_model()
+        sample = self.dummy_sample_deter * scheduler.init_noise_sigma
+        sample = sample.to(torch_device)
+
+        for i, t in enumerate(scheduler.timesteps):
+            sample = scheduler.scale_model_input(sample, t)
+
+            model_output = model(sample, t)
+
+            output = scheduler.step(model_output, t, sample)
+            sample = output.prev_sample
+
+        result_sum = torch.sum(torch.abs(sample))
+        result_mean = torch.mean(torch.abs(sample))
+
+        assert abs(result_sum.item() - 1006.388) < 1e-2
+        assert abs(result_mean.item() - 1.31) < 1e-3

tests/schedulers/test_scheduler_pndm.py

+import tempfile
+
+import torch
+
+from diffusers import PNDMScheduler
+
+from .test_schedulers import SchedulerCommonTest
+
+
+class PNDMSchedulerTest(SchedulerCommonTest):
+    scheduler_classes = (PNDMScheduler,)
+    forward_default_kwargs = (("num_inference_steps", 50),)
+
+    def get_scheduler_config(self, **kwargs):
+        config = {
+            "num_train_timesteps": 1000,
+            "beta_start": 0.0001,
+            "beta_end": 0.02,
+            "beta_schedule": "linear",
+        }
+
+        config.update(**kwargs)
+        return config
+
+    def check_over_configs(self, time_step=0, **config):
+        kwargs = dict(self.forward_default_kwargs)
+        num_inference_steps = kwargs.pop("num_inference_steps", None)
+        sample = self.dummy_sample
+        residual = 0.1 * sample
+        dummy_past_residuals = [residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]
+
+        for scheduler_class in self.scheduler_classes:
+            scheduler_config = self.get_scheduler_config(**config)
+            scheduler = scheduler_class(**scheduler_config)
+            scheduler.set_timesteps(num_inference_steps)
+            # copy over dummy past residuals
+            scheduler.ets = dummy_past_residuals[:]
+
+            with tempfile.TemporaryDirectory() as tmpdirname:
+                scheduler.save_config(tmpdirname)
+                new_scheduler = scheduler_class.from_pretrained(tmpdirname)
+                new_scheduler.set_timesteps(num_inference_steps)
+                # copy over dummy past residuals
+                new_scheduler.ets = dummy_past_residuals[:]
+
+            output = scheduler.step_prk(residual, time_step, sample, **kwargs).prev_sample
+            new_output = new_scheduler.step_prk(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_plms(residual, time_step, sample, **kwargs).prev_sample
+            new_output = new_scheduler.step_plms(residual, time_step, sample, **kwargs).prev_sample
+
+            assert torch.sum(torch.abs(output - new_output)) < 1e-5, "Scheduler outputs are not identical"
+
+    def test_from_save_pretrained(self):
+        pass
+
+    def check_over_forward(self, time_step=0, **forward_kwargs):
+        kwargs = dict(self.forward_default_kwargs)
+        num_inference_steps = kwargs.pop("num_inference_steps", None)
+        sample = self.dummy_sample
+        residual = 0.1 * sample
+        dummy_past_residuals = [residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]
+
+        for scheduler_class in self.scheduler_classes:
+            scheduler_config = self.get_scheduler_config()
+            scheduler = scheduler_class(**scheduler_config)
+            scheduler.set_timesteps(num_inference_steps)
+
+            # copy over dummy past residuals (must be after setting timesteps)
+            scheduler.ets = dummy_past_residuals[:]
+
+            with tempfile.TemporaryDirectory() as tmpdirname:
+                scheduler.save_config(tmpdirname)
+                new_scheduler = scheduler_class.from_pretrained(tmpdirname)
+                # copy over dummy past residuals
+                new_scheduler.set_timesteps(num_inference_steps)
+
+                # copy over dummy past residual (must be after setting timesteps)
+                new_scheduler.ets = dummy_past_residuals[:]
+
+            output = scheduler.step_prk(residual, time_step, sample, **kwargs).prev_sample
+            new_output = new_scheduler.step_prk(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_plms(residual, time_step, sample, **kwargs).prev_sample
+            new_output = new_scheduler.step_plms(residual, time_step, sample, **kwargs).prev_sample
+
+            assert torch.sum(torch.abs(output - new_output)) < 1e-5, "Scheduler outputs are not identical"
+
+    def full_loop(self, **config):
+        scheduler_class = self.scheduler_classes[0]
+        scheduler_config = self.get_scheduler_config(**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, t, sample).prev_sample
+
+        for i, t in enumerate(scheduler.plms_timesteps):
+            residual = model(sample, t)
+            sample = scheduler.step_plms(residual, t, sample).prev_sample
+
+        return sample
+
+    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
+
+            # copy over dummy past residuals (must be done after set_timesteps)
+            dummy_past_residuals = [residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]
+            scheduler.ets = dummy_past_residuals[:]
+
+            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_steps_offset(self):
+        for steps_offset in [0, 1]:
+            self.check_over_configs(steps_offset=steps_offset)
+
+        scheduler_class = self.scheduler_classes[0]
+        scheduler_config = self.get_scheduler_config(steps_offset=1)
+        scheduler = scheduler_class(**scheduler_config)
+        scheduler.set_timesteps(10)
+        assert torch.equal(
+            scheduler.timesteps,
+            torch.LongTensor(
+                [901, 851, 851, 801, 801, 751, 751, 701, 701, 651, 651, 601, 601, 501, 401, 301, 201, 101, 1]
+            ),
+        )
+
+    def test_betas(self):
+        for beta_start, beta_end in zip([0.0001, 0.001], [0.002, 0.02]):
+            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_prediction_type(self):
+        for prediction_type in ["epsilon", "v_prediction"]:
+            self.check_over_configs(prediction_type=prediction_type)
+
+    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(num_inference_steps=num_inference_steps)
+
+    def test_pow_of_3_inference_steps(self):
+        # earlier version of set_timesteps() caused an error indexing alpha's with inference steps as power of 3
+        num_inference_steps = 27
+
+        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)
+
+            scheduler.set_timesteps(num_inference_steps)
+
+            # before power of 3 fix, would error on first step, so we only need to do two
+            for i, t in enumerate(scheduler.prk_timesteps[:2]):
+                sample = scheduler.step_prk(residual, t, sample).prev_sample
+
+    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):
+        sample = self.full_loop()
+        result_sum = torch.sum(torch.abs(sample))
+        result_mean = torch.mean(torch.abs(sample))
+
+        assert abs(result_sum.item() - 198.1318) < 1e-2
+        assert abs(result_mean.item() - 0.2580) < 1e-3
+
+    def test_full_loop_with_v_prediction(self):
+        sample = self.full_loop(prediction_type="v_prediction")
+        result_sum = torch.sum(torch.abs(sample))
+        result_mean = torch.mean(torch.abs(sample))
+
+        assert abs(result_sum.item() - 67.3986) < 1e-2
+        assert abs(result_mean.item() - 0.0878) < 1e-3
+
+    def test_full_loop_with_set_alpha_to_one(self):
+        # We specify different beta, so that the first alpha is 0.99
+        sample = self.full_loop(set_alpha_to_one=True, beta_start=0.01)
+        result_sum = torch.sum(torch.abs(sample))
+        result_mean = torch.mean(torch.abs(sample))
+
+        assert abs(result_sum.item() - 230.0399) < 1e-2
+        assert abs(result_mean.item() - 0.2995) < 1e-3
+
+    def test_full_loop_with_no_set_alpha_to_one(self):
+        # We specify different beta, so that the first alpha is 0.99
+        sample = self.full_loop(set_alpha_to_one=False, beta_start=0.01)
+        result_sum = torch.sum(torch.abs(sample))
+        result_mean = torch.mean(torch.abs(sample))
+
+        assert abs(result_sum.item() - 186.9482) < 1e-2
+        assert abs(result_mean.item() - 0.2434) < 1e-3

tests/schedulers/test_scheduler_score_sde_ve.py

+import tempfile
+import unittest
+
+import numpy as np
+import torch
+
+from diffusers import ScoreSdeVeScheduler
+
+
+class ScoreSdeVeSchedulerTest(unittest.TestCase):
+    # TODO adapt with class SchedulerCommonTest (scheduler needs Numpy Integration)
+    scheduler_classes = (ScoreSdeVeScheduler,)
+    forward_default_kwargs = ()
+
+    @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,
+        }
+
+        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_pretrained(tmpdirname)
+
+            output = scheduler.step_pred(
+                residual, time_step, sample, generator=torch.manual_seed(0), **kwargs
+            ).prev_sample
+            new_output = new_scheduler.step_pred(
+                residual, time_step, sample, generator=torch.manual_seed(0), **kwargs
+            ).prev_sample
+
+            assert torch.sum(torch.abs(output - new_output)) < 1e-5, "Scheduler outputs are not identical"
+
+            output = scheduler.step_correct(residual, sample, generator=torch.manual_seed(0), **kwargs).prev_sample
+            new_output = new_scheduler.step_correct(
+                residual, sample, generator=torch.manual_seed(0), **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_pretrained(tmpdirname)
+
+            output = scheduler.step_pred(
+                residual, time_step, sample, generator=torch.manual_seed(0), **kwargs
+            ).prev_sample
+            new_output = new_scheduler.step_pred(
+                residual, time_step, sample, generator=torch.manual_seed(0), **kwargs
+            ).prev_sample
+
+            assert torch.sum(torch.abs(output - new_output)) < 1e-5, "Scheduler outputs are not identical"
+
+            output = scheduler.step_correct(residual, sample, generator=torch.manual_seed(0), **kwargs).prev_sample
+            new_output = new_scheduler.step_correct(
+                residual, sample, generator=torch.manual_seed(0), **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)
+        generator = torch.manual_seed(0)
+
+        for i, t in enumerate(scheduler.timesteps):
+            sigma_t = scheduler.sigmas[i]
+
+            for _ in range(scheduler.config.correct_steps):
+                with torch.no_grad():
+                    model_output = model(sample, sigma_t)
+                sample = scheduler.step_correct(model_output, sample, generator=generator, **kwargs).prev_sample
+
+            with torch.no_grad():
+                model_output = model(sample, sigma_t)
+
+            output = scheduler.step_pred(model_output, t, sample, generator=generator, **kwargs)
+            sample, _ = output.prev_sample, output.prev_sample_mean
+
+        result_sum = torch.sum(torch.abs(sample))
+        result_mean = torch.mean(torch.abs(sample))
+
+        assert np.isclose(result_sum.item(), 14372758528.0)
+        assert np.isclose(result_mean.item(), 18714530.0)
+
+    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, generator=torch.manual_seed(0), **kwargs).prev_sample
+            output_1 = scheduler.step_pred(residual, 1, sample, generator=torch.manual_seed(0), **kwargs).prev_sample
+
+            self.assertEqual(output_0.shape, sample.shape)
+            self.assertEqual(output_0.shape, output_1.shape)

tests/schedulers/test_scheduler_unclip.py

+import torch
+
+from diffusers import UnCLIPScheduler
+
+from .test_schedulers import SchedulerCommonTest
+
+
+# UnCLIPScheduler is a modified DDPMScheduler with a subset of the configuration.
+class UnCLIPSchedulerTest(SchedulerCommonTest):
+    scheduler_classes = (UnCLIPScheduler,)
+
+    def get_scheduler_config(self, **kwargs):
+        config = {
+            "num_train_timesteps": 1000,
+            "variance_type": "fixed_small_log",
+            "clip_sample": True,
+            "clip_sample_range": 1.0,
+            "prediction_type": "epsilon",
+        }
+
+        config.update(**kwargs)
+        return config
+
+    def test_timesteps(self):
+        for timesteps in [1, 5, 100, 1000]:
+            self.check_over_configs(num_train_timesteps=timesteps)
+
+    def test_variance_type(self):
+        for variance in ["fixed_small_log", "learned_range"]:
+            self.check_over_configs(variance_type=variance)
+
+    def test_clip_sample(self):
+        for clip_sample in [True, False]:
+            self.check_over_configs(clip_sample=clip_sample)
+
+    def test_clip_sample_range(self):
+        for clip_sample_range in [1, 5, 10, 20]:
+            self.check_over_configs(clip_sample_range=clip_sample_range)
+
+    def test_prediction_type(self):
+        for prediction_type in ["epsilon", "sample"]:
+            self.check_over_configs(prediction_type=prediction_type)
+
+    def test_time_indices(self):
+        for time_step in [0, 500, 999]:
+            for prev_timestep in [None, 5, 100, 250, 500, 750]:
+                if prev_timestep is not None and prev_timestep >= time_step:
+                    continue
+
+                self.check_over_forward(time_step=time_step, prev_timestep=prev_timestep)
+
+    def test_variance_fixed_small_log(self):
+        scheduler_class = self.scheduler_classes[0]
+        scheduler_config = self.get_scheduler_config(variance_type="fixed_small_log")
+        scheduler = scheduler_class(**scheduler_config)
+
+        assert torch.sum(torch.abs(scheduler._get_variance(0) - 1.0000e-10)) < 1e-5
+        assert torch.sum(torch.abs(scheduler._get_variance(487) - 0.0549625)) < 1e-5
+        assert torch.sum(torch.abs(scheduler._get_variance(999) - 0.9994987)) < 1e-5
+
+    def test_variance_learned_range(self):
+        scheduler_class = self.scheduler_classes[0]
+        scheduler_config = self.get_scheduler_config(variance_type="learned_range")
+        scheduler = scheduler_class(**scheduler_config)
+
+        predicted_variance = 0.5
+
+        assert scheduler._get_variance(1, predicted_variance=predicted_variance) - -10.1712790 < 1e-5
+        assert scheduler._get_variance(487, predicted_variance=predicted_variance) - -5.7998052 < 1e-5
+        assert scheduler._get_variance(999, predicted_variance=predicted_variance) - -0.0010011 < 1e-5
+
+    def test_full_loop(self):
+        scheduler_class = self.scheduler_classes[0]
+        scheduler_config = self.get_scheduler_config()
+        scheduler = scheduler_class(**scheduler_config)
+
+        timesteps = scheduler.timesteps
+
+        model = self.dummy_model()
+        sample = self.dummy_sample_deter
+        generator = torch.manual_seed(0)
+
+        for i, t in enumerate(timesteps):
+            # 1. predict noise residual
+            residual = model(sample, t)
+
+            # 2. predict previous mean of sample x_t-1
+            pred_prev_sample = scheduler.step(residual, t, sample, generator=generator).prev_sample
+
+            sample = pred_prev_sample
+
+        result_sum = torch.sum(torch.abs(sample))
+        result_mean = torch.mean(torch.abs(sample))
+
+        assert abs(result_sum.item() - 252.2682495) < 1e-2
+        assert abs(result_mean.item() - 0.3284743) < 1e-3
+
+    def test_full_loop_skip_timesteps(self):
+        scheduler_class = self.scheduler_classes[0]
+        scheduler_config = self.get_scheduler_config()
+        scheduler = scheduler_class(**scheduler_config)
+
+        scheduler.set_timesteps(25)
+
+        timesteps = scheduler.timesteps
+
+        model = self.dummy_model()
+        sample = self.dummy_sample_deter
+        generator = torch.manual_seed(0)
+
+        for i, t in enumerate(timesteps):
+            # 1. predict noise residual
+            residual = model(sample, t)
+
+            if i + 1 == timesteps.shape[0]:
+                prev_timestep = None
+            else:
+                prev_timestep = timesteps[i + 1]
+
+            # 2. predict previous mean of sample x_t-1
+            pred_prev_sample = scheduler.step(
+                residual, t, sample, prev_timestep=prev_timestep, generator=generator
+            ).prev_sample
+
+            sample = pred_prev_sample
+
+        result_sum = torch.sum(torch.abs(sample))
+        result_mean = torch.mean(torch.abs(sample))
+
+        assert abs(result_sum.item() - 258.2044983) < 1e-2
+        assert abs(result_mean.item() - 0.3362038) < 1e-3
+
+    def test_trained_betas(self):
+        pass
+
+    def test_add_noise_device(self):
+        pass

tests/schedulers/test_scheduler_unipc.py

+import tempfile
+
+import torch
+
+from diffusers import (
+    DEISMultistepScheduler,
+    DPMSolverMultistepScheduler,
+    DPMSolverSinglestepScheduler,
+    UniPCMultistepScheduler,
+)
+
+from .test_schedulers import SchedulerCommonTest
+
+
+class UniPCMultistepSchedulerTest(SchedulerCommonTest):
+    scheduler_classes = (UniPCMultistepScheduler,)
+    forward_default_kwargs = (("num_inference_steps", 25),)
+
+    def get_scheduler_config(self, **kwargs):
+        config = {
+            "num_train_timesteps": 1000,
+            "beta_start": 0.0001,
+            "beta_end": 0.02,
+            "beta_schedule": "linear",
+            "solver_order": 2,
+            "solver_type": "bh1",
+        }
+
+        config.update(**kwargs)
+        return config
+
+    def check_over_configs(self, time_step=0, **config):
+        kwargs = dict(self.forward_default_kwargs)
+        num_inference_steps = kwargs.pop("num_inference_steps", None)
+        sample = self.dummy_sample
+        residual = 0.1 * sample
+        dummy_past_residuals = [residual + 0.2, residual + 0.15, residual + 0.10]
+
+        for scheduler_class in self.scheduler_classes:
+            scheduler_config = self.get_scheduler_config(**config)
+            scheduler = scheduler_class(**scheduler_config)
+            scheduler.set_timesteps(num_inference_steps)
+            # copy over dummy past residuals
+            scheduler.model_outputs = dummy_past_residuals[: scheduler.config.solver_order]
+
+            with tempfile.TemporaryDirectory() as tmpdirname:
+                scheduler.save_config(tmpdirname)
+                new_scheduler = scheduler_class.from_pretrained(tmpdirname)
+                new_scheduler.set_timesteps(num_inference_steps)
+                # copy over dummy past residuals
+                new_scheduler.model_outputs = dummy_past_residuals[: new_scheduler.config.solver_order]
+
+            output, new_output = sample, sample
+            for t in range(time_step, time_step + scheduler.config.solver_order + 1):
+                output = scheduler.step(residual, t, output, **kwargs).prev_sample
+                new_output = new_scheduler.step(residual, t, new_output, **kwargs).prev_sample
+
+                assert torch.sum(torch.abs(output - new_output)) < 1e-5, "Scheduler outputs are not identical"
+
+    def check_over_forward(self, time_step=0, **forward_kwargs):
+        kwargs = dict(self.forward_default_kwargs)
+        num_inference_steps = kwargs.pop("num_inference_steps", None)
+        sample = self.dummy_sample
+        residual = 0.1 * sample
+        dummy_past_residuals = [residual + 0.2, residual + 0.15, residual + 0.10]
+
+        for scheduler_class in self.scheduler_classes:
+            scheduler_config = self.get_scheduler_config()
+            scheduler = scheduler_class(**scheduler_config)
+            scheduler.set_timesteps(num_inference_steps)
+
+            # copy over dummy past residuals (must be after setting timesteps)
+            scheduler.model_outputs = dummy_past_residuals[: scheduler.config.solver_order]
+
+            with tempfile.TemporaryDirectory() as tmpdirname:
+                scheduler.save_config(tmpdirname)
+                new_scheduler = scheduler_class.from_pretrained(tmpdirname)
+                # copy over dummy past residuals
+                new_scheduler.set_timesteps(num_inference_steps)
+
+                # copy over dummy past residual (must be after setting timesteps)
+                new_scheduler.model_outputs = dummy_past_residuals[: new_scheduler.config.solver_order]
+
+            output = scheduler.step(residual, time_step, sample, **kwargs).prev_sample
+            new_output = new_scheduler.step(residual, time_step, sample, **kwargs).prev_sample
+
+            assert torch.sum(torch.abs(output - new_output)) < 1e-5, "Scheduler outputs are not identical"
+
+    def full_loop(self, scheduler=None, **config):
+        if scheduler is None:
+            scheduler_class = self.scheduler_classes[0]
+            scheduler_config = self.get_scheduler_config(**config)
+            scheduler = scheduler_class(**scheduler_config)
+
+        scheduler_class = self.scheduler_classes[0]
+        scheduler_config = self.get_scheduler_config(**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.timesteps):
+            residual = model(sample, t)
+            sample = scheduler.step(residual, t, sample).prev_sample
+
+        return sample
+
+    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
+
+            # copy over dummy past residuals (must be done after set_timesteps)
+            dummy_past_residuals = [residual + 0.2, residual + 0.15, residual + 0.10]
+            scheduler.model_outputs = dummy_past_residuals[: scheduler.config.solver_order]
+
+            time_step_0 = scheduler.timesteps[5]
+            time_step_1 = scheduler.timesteps[6]
+
+            output_0 = scheduler.step(residual, time_step_0, sample, **kwargs).prev_sample
+            output_1 = scheduler.step(residual, time_step_1, sample, **kwargs).prev_sample
+
+            self.assertEqual(output_0.shape, sample.shape)
+            self.assertEqual(output_0.shape, output_1.shape)
+
+    def test_switch(self):
+        # make sure that iterating over schedulers with same config names gives same results
+        # for defaults
+        scheduler = UniPCMultistepScheduler(**self.get_scheduler_config())
+        sample = self.full_loop(scheduler=scheduler)
+        result_mean = torch.mean(torch.abs(sample))
+
+        assert abs(result_mean.item() - 0.2521) < 1e-3
+
+        scheduler = DPMSolverSinglestepScheduler.from_config(scheduler.config)
+        scheduler = DEISMultistepScheduler.from_config(scheduler.config)
+        scheduler = DPMSolverMultistepScheduler.from_config(scheduler.config)
+        scheduler = UniPCMultistepScheduler.from_config(scheduler.config)
+
+        sample = self.full_loop(scheduler=scheduler)
+        result_mean = torch.mean(torch.abs(sample))
+
+        assert abs(result_mean.item() - 0.2521) < 1e-3
+
+    def test_timesteps(self):
+        for timesteps in [25, 50, 100, 999, 1000]:
+            self.check_over_configs(num_train_timesteps=timesteps)
+
+    def test_thresholding(self):
+        self.check_over_configs(thresholding=False)
+        for order in [1, 2, 3]:
+            for solver_type in ["bh1", "bh2"]:
+                for threshold in [0.5, 1.0, 2.0]:
+                    for prediction_type in ["epsilon", "sample"]:
+                        self.check_over_configs(
+                            thresholding=True,
+                            prediction_type=prediction_type,
+                            sample_max_value=threshold,
+                            solver_order=order,
+                            solver_type=solver_type,
+                        )
+
+    def test_prediction_type(self):
+        for prediction_type in ["epsilon", "v_prediction"]:
+            self.check_over_configs(prediction_type=prediction_type)
+
+    def test_solver_order_and_type(self):
+        for solver_type in ["bh1", "bh2"]:
+            for order in [1, 2, 3]:
+                for prediction_type in ["epsilon", "sample"]:
+                    self.check_over_configs(
+                        solver_order=order,
+                        solver_type=solver_type,
+                        prediction_type=prediction_type,
+                    )
+                    sample = self.full_loop(
+                        solver_order=order,
+                        solver_type=solver_type,
+                        prediction_type=prediction_type,
+                    )
+                    assert not torch.isnan(sample).any(), "Samples have nan numbers"
+
+    def test_lower_order_final(self):
+        self.check_over_configs(lower_order_final=True)
+        self.check_over_configs(lower_order_final=False)
+
+    def test_inference_steps(self):
+        for num_inference_steps in [1, 2, 3, 5, 10, 50, 100, 999, 1000]:
+            self.check_over_forward(num_inference_steps=num_inference_steps, time_step=0)
+
+    def test_full_loop_no_noise(self):
+        sample = self.full_loop()
+        result_mean = torch.mean(torch.abs(sample))
+
+        assert abs(result_mean.item() - 0.2521) < 1e-3
+
+    def test_full_loop_with_v_prediction(self):
+        sample = self.full_loop(prediction_type="v_prediction")
+        result_mean = torch.mean(torch.abs(sample))
+
+        assert abs(result_mean.item() - 0.1096) < 1e-3
+
+    def test_fp16_support(self):
+        scheduler_class = self.scheduler_classes[0]
+        scheduler_config = self.get_scheduler_config(thresholding=True, dynamic_thresholding_ratio=0)
+        scheduler = scheduler_class(**scheduler_config)
+
+        num_inference_steps = 10
+        model = self.dummy_model()
+        sample = self.dummy_sample_deter.half()
+        scheduler.set_timesteps(num_inference_steps)
+
+        for i, t in enumerate(scheduler.timesteps):
+            residual = model(sample, t)
+            sample = scheduler.step(residual, t, sample).prev_sample
+
+        assert sample.dtype == torch.float16

tests/schedulers/test_scheduler_vq_diffusion.py

+import torch
+import torch.nn.functional as F
+
+from diffusers import VQDiffusionScheduler
+
+from .test_schedulers import SchedulerCommonTest
+
+
+class VQDiffusionSchedulerTest(SchedulerCommonTest):
+    scheduler_classes = (VQDiffusionScheduler,)
+
+    def get_scheduler_config(self, **kwargs):
+        config = {
+            "num_vec_classes": 4097,
+            "num_train_timesteps": 100,
+        }
+
+        config.update(**kwargs)
+        return config
+
+    def dummy_sample(self, num_vec_classes):
+        batch_size = 4
+        height = 8
+        width = 8
+
+        sample = torch.randint(0, num_vec_classes, (batch_size, height * width))
+
+        return sample
+
+    @property
+    def dummy_sample_deter(self):
+        assert False
+
+    def dummy_model(self, num_vec_classes):
+        def model(sample, t, *args):
+            batch_size, num_latent_pixels = sample.shape
+            logits = torch.rand((batch_size, num_vec_classes - 1, num_latent_pixels))
+            return_value = F.log_softmax(logits.double(), dim=1).float()
+            return return_value
+
+        return model
+
+    def test_timesteps(self):
+        for timesteps in [2, 5, 100, 1000]:
+            self.check_over_configs(num_train_timesteps=timesteps)
+
+    def test_num_vec_classes(self):
+        for num_vec_classes in [5, 100, 1000, 4000]:
+            self.check_over_configs(num_vec_classes=num_vec_classes)
+
+    def test_time_indices(self):
+        for t in [0, 50, 99]:
+            self.check_over_forward(time_step=t)
+
+    def test_add_noise_device(self):
+        pass

tests/schedulers/test_schedulers.py

+# coding=utf-8
+# Copyright 2023 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 json
+import os
+import tempfile
+import unittest
+from typing import Dict, List, Tuple
+
+import numpy as np
+import torch
+
+import diffusers
+from diffusers import (
+    EulerAncestralDiscreteScheduler,
+    EulerDiscreteScheduler,
+    IPNDMScheduler,
+    LMSDiscreteScheduler,
+    VQDiffusionScheduler,
+    logging,
+)
+from diffusers.configuration_utils import ConfigMixin, register_to_config
+from diffusers.schedulers.scheduling_utils import SchedulerMixin
+from diffusers.utils import torch_device
+from diffusers.utils.testing_utils import CaptureLogger
+
+
+torch.backends.cuda.matmul.allow_tf32 = False
+
+
+class SchedulerObject(SchedulerMixin, ConfigMixin):
+    config_name = "config.json"
+
+    @register_to_config
+    def __init__(
+        self,
+        a=2,
+        b=5,
+        c=(2, 5),
+        d="for diffusion",
+        e=[1, 3],
+    ):
+        pass
+
+
+class SchedulerObject2(SchedulerMixin, ConfigMixin):
+    config_name = "config.json"
+
+    @register_to_config
+    def __init__(
+        self,
+        a=2,
+        b=5,
+        c=(2, 5),
+        d="for diffusion",
+        f=[1, 3],
+    ):
+        pass
+
+
+class SchedulerObject3(SchedulerMixin, ConfigMixin):
+    config_name = "config.json"
+
+    @register_to_config
+    def __init__(
+        self,
+        a=2,
+        b=5,
+        c=(2, 5),
+        d="for diffusion",
+        e=[1, 3],
+        f=[1, 3],
+    ):
+        pass
+
+
+class SchedulerBaseTests(unittest.TestCase):
+    def test_save_load_from_different_config(self):
+        obj = SchedulerObject()
+
+        # mock add obj class to `diffusers`
+        setattr(diffusers, "SchedulerObject", SchedulerObject)
+        logger = logging.get_logger("diffusers.configuration_utils")
+
+        with tempfile.TemporaryDirectory() as tmpdirname:
+            obj.save_config(tmpdirname)
+            with CaptureLogger(logger) as cap_logger_1:
+                config = SchedulerObject2.load_config(tmpdirname)
+                new_obj_1 = SchedulerObject2.from_config(config)
+
+            # now save a config parameter that is not expected
+            with open(os.path.join(tmpdirname, SchedulerObject.config_name), "r") as f:
+                data = json.load(f)
+                data["unexpected"] = True
+
+            with open(os.path.join(tmpdirname, SchedulerObject.config_name), "w") as f:
+                json.dump(data, f)
+
+            with CaptureLogger(logger) as cap_logger_2:
+                config = SchedulerObject.load_config(tmpdirname)
+                new_obj_2 = SchedulerObject.from_config(config)
+
+            with CaptureLogger(logger) as cap_logger_3:
+                config = SchedulerObject2.load_config(tmpdirname)
+                new_obj_3 = SchedulerObject2.from_config(config)
+
+        assert new_obj_1.__class__ == SchedulerObject2
+        assert new_obj_2.__class__ == SchedulerObject
+        assert new_obj_3.__class__ == SchedulerObject2
+
+        assert cap_logger_1.out == ""
+        assert (
+            cap_logger_2.out
+            == "The config attributes {'unexpected': True} were passed to SchedulerObject, but are not expected and"
+            " will"
+            " be ignored. Please verify your config.json configuration file.\n"
+        )
+        assert cap_logger_2.out.replace("SchedulerObject", "SchedulerObject2") == cap_logger_3.out
+
+    def test_save_load_compatible_schedulers(self):
+        SchedulerObject2._compatibles = ["SchedulerObject"]
+        SchedulerObject._compatibles = ["SchedulerObject2"]
+
+        obj = SchedulerObject()
+
+        # mock add obj class to `diffusers`
+        setattr(diffusers, "SchedulerObject", SchedulerObject)
+        setattr(diffusers, "SchedulerObject2", SchedulerObject2)
+        logger = logging.get_logger("diffusers.configuration_utils")
+
+        with tempfile.TemporaryDirectory() as tmpdirname:
+            obj.save_config(tmpdirname)
+
+            # now save a config parameter that is expected by another class, but not origin class
+            with open(os.path.join(tmpdirname, SchedulerObject.config_name), "r") as f:
+                data = json.load(f)
+                data["f"] = [0, 0]
+                data["unexpected"] = True
+
+            with open(os.path.join(tmpdirname, SchedulerObject.config_name), "w") as f:
+                json.dump(data, f)
+
+            with CaptureLogger(logger) as cap_logger:
+                config = SchedulerObject.load_config(tmpdirname)
+                new_obj = SchedulerObject.from_config(config)
+
+        assert new_obj.__class__ == SchedulerObject
+
+        assert (
+            cap_logger.out
+            == "The config attributes {'unexpected': True} were passed to SchedulerObject, but are not expected and"
+            " will"
+            " be ignored. Please verify your config.json configuration file.\n"
+        )
+
+    def test_save_load_from_different_config_comp_schedulers(self):
+        SchedulerObject3._compatibles = ["SchedulerObject", "SchedulerObject2"]
+        SchedulerObject2._compatibles = ["SchedulerObject", "SchedulerObject3"]
+        SchedulerObject._compatibles = ["SchedulerObject2", "SchedulerObject3"]
+
+        obj = SchedulerObject()
+
+        # mock add obj class to `diffusers`
+        setattr(diffusers, "SchedulerObject", SchedulerObject)
+        setattr(diffusers, "SchedulerObject2", SchedulerObject2)
+        setattr(diffusers, "SchedulerObject3", SchedulerObject3)
+        logger = logging.get_logger("diffusers.configuration_utils")
+        logger.setLevel(diffusers.logging.INFO)
+
+        with tempfile.TemporaryDirectory() as tmpdirname:
+            obj.save_config(tmpdirname)
+
+            with CaptureLogger(logger) as cap_logger_1:
+                config = SchedulerObject.load_config(tmpdirname)
+                new_obj_1 = SchedulerObject.from_config(config)
+
+            with CaptureLogger(logger) as cap_logger_2:
+                config = SchedulerObject2.load_config(tmpdirname)
+                new_obj_2 = SchedulerObject2.from_config(config)
+
+            with CaptureLogger(logger) as cap_logger_3:
+                config = SchedulerObject3.load_config(tmpdirname)
+                new_obj_3 = SchedulerObject3.from_config(config)
+
+        assert new_obj_1.__class__ == SchedulerObject
+        assert new_obj_2.__class__ == SchedulerObject2
+        assert new_obj_3.__class__ == SchedulerObject3
+
+        assert cap_logger_1.out == ""
+        assert cap_logger_2.out == "{'f'} was not found in config. Values will be initialized to default values.\n"
+        assert cap_logger_3.out == "{'f'} was not found in config. Values will be initialized to default values.\n"
+
+
+class SchedulerCommonTest(unittest.TestCase):
+    scheduler_classes = ()
+    forward_default_kwargs = ()
+
+    @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 get_scheduler_config(self):
+        raise NotImplementedError
+
+    def dummy_model(self):
+        def model(sample, t, *args):
+            return sample * t / (t + 1)
+
+        return model
+
+    def check_over_configs(self, time_step=0, **config):
+        kwargs = dict(self.forward_default_kwargs)
+
+        num_inference_steps = kwargs.pop("num_inference_steps", None)
+
+        for scheduler_class in self.scheduler_classes:
+            # TODO(Suraj) - delete the following two lines once DDPM, DDIM, and PNDM have timesteps casted to float by default
+            if scheduler_class in (EulerAncestralDiscreteScheduler, EulerDiscreteScheduler, LMSDiscreteScheduler):
+                time_step = float(time_step)
+
+            scheduler_config = self.get_scheduler_config(**config)
+            scheduler = scheduler_class(**scheduler_config)
+
+            if scheduler_class == VQDiffusionScheduler:
+                num_vec_classes = scheduler_config["num_vec_classes"]
+                sample = self.dummy_sample(num_vec_classes)
+                model = self.dummy_model(num_vec_classes)
+                residual = model(sample, time_step)
+            else:
+                sample = self.dummy_sample
+                residual = 0.1 * sample
+
+            with tempfile.TemporaryDirectory() as tmpdirname:
+                scheduler.save_config(tmpdirname)
+                new_scheduler = scheduler_class.from_pretrained(tmpdirname)
+
+            if num_inference_steps is not None and hasattr(scheduler, "set_timesteps"):
+                scheduler.set_timesteps(num_inference_steps)
+                new_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
+
+            # Make sure `scale_model_input` is invoked to prevent a warning
+            if scheduler_class != VQDiffusionScheduler:
+                _ = scheduler.scale_model_input(sample, 0)
+                _ = new_scheduler.scale_model_input(sample, 0)
+
+            # Set the seed before step() as some schedulers are stochastic like EulerAncestralDiscreteScheduler, EulerDiscreteScheduler
+            if "generator" in set(inspect.signature(scheduler.step).parameters.keys()):
+                kwargs["generator"] = torch.manual_seed(0)
+            output = scheduler.step(residual, time_step, sample, **kwargs).prev_sample
+
+            if "generator" in set(inspect.signature(scheduler.step).parameters.keys()):
+                kwargs["generator"] = torch.manual_seed(0)
+            new_output = new_scheduler.step(residual, time_step, sample, **kwargs).prev_sample
+
+            assert torch.sum(torch.abs(output - new_output)) < 1e-5, "Scheduler outputs are not identical"
+
+    def check_over_forward(self, time_step=0, **forward_kwargs):
+        kwargs = dict(self.forward_default_kwargs)
+        kwargs.update(forward_kwargs)
+
+        num_inference_steps = kwargs.pop("num_inference_steps", None)
+
+        for scheduler_class in self.scheduler_classes:
+            if scheduler_class in (EulerAncestralDiscreteScheduler, EulerDiscreteScheduler, LMSDiscreteScheduler):
+                time_step = float(time_step)
+
+            scheduler_config = self.get_scheduler_config()
+            scheduler = scheduler_class(**scheduler_config)
+
+            if scheduler_class == VQDiffusionScheduler:
+                num_vec_classes = scheduler_config["num_vec_classes"]
+                sample = self.dummy_sample(num_vec_classes)
+                model = self.dummy_model(num_vec_classes)
+                residual = model(sample, time_step)
+            else:
+                sample = self.dummy_sample
+                residual = 0.1 * sample
+
+            with tempfile.TemporaryDirectory() as tmpdirname:
+                scheduler.save_config(tmpdirname)
+                new_scheduler = scheduler_class.from_pretrained(tmpdirname)
+
+            if num_inference_steps is not None and hasattr(scheduler, "set_timesteps"):
+                scheduler.set_timesteps(num_inference_steps)
+                new_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
+
+            if "generator" in set(inspect.signature(scheduler.step).parameters.keys()):
+                kwargs["generator"] = torch.manual_seed(0)
+            output = scheduler.step(residual, time_step, sample, **kwargs).prev_sample
+
+            if "generator" in set(inspect.signature(scheduler.step).parameters.keys()):
+                kwargs["generator"] = torch.manual_seed(0)
+            new_output = new_scheduler.step(residual, time_step, sample, **kwargs).prev_sample
+
+            assert torch.sum(torch.abs(output - new_output)) < 1e-5, "Scheduler outputs are not identical"
+
+    def test_from_save_pretrained(self):
+        kwargs = dict(self.forward_default_kwargs)
+
+        num_inference_steps = kwargs.pop("num_inference_steps", None)
+
+        for scheduler_class in self.scheduler_classes:
+            timestep = 1
+            if scheduler_class in (EulerAncestralDiscreteScheduler, EulerDiscreteScheduler, LMSDiscreteScheduler):
+                timestep = float(timestep)
+
+            scheduler_config = self.get_scheduler_config()
+            scheduler = scheduler_class(**scheduler_config)
+
+            if scheduler_class == VQDiffusionScheduler:
+                num_vec_classes = scheduler_config["num_vec_classes"]
+                sample = self.dummy_sample(num_vec_classes)
+                model = self.dummy_model(num_vec_classes)
+                residual = model(sample, timestep)
+            else:
+                sample = self.dummy_sample
+                residual = 0.1 * sample
+
+            with tempfile.TemporaryDirectory() as tmpdirname:
+                scheduler.save_config(tmpdirname)
+                new_scheduler = scheduler_class.from_pretrained(tmpdirname)
+
+            if num_inference_steps is not None and hasattr(scheduler, "set_timesteps"):
+                scheduler.set_timesteps(num_inference_steps)
+                new_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
+
+            if "generator" in set(inspect.signature(scheduler.step).parameters.keys()):
+                kwargs["generator"] = torch.manual_seed(0)
+            output = scheduler.step(residual, timestep, sample, **kwargs).prev_sample
+
+            if "generator" in set(inspect.signature(scheduler.step).parameters.keys()):
+                kwargs["generator"] = torch.manual_seed(0)
+            new_output = new_scheduler.step(residual, timestep, sample, **kwargs).prev_sample
+
+            assert torch.sum(torch.abs(output - new_output)) < 1e-5, "Scheduler outputs are not identical"
+
+    def test_compatibles(self):
+        for scheduler_class in self.scheduler_classes:
+            scheduler_config = self.get_scheduler_config()
+
+            scheduler = scheduler_class(**scheduler_config)
+
+            assert all(c is not None for c in scheduler.compatibles)
+
+            for comp_scheduler_cls in scheduler.compatibles:
+                comp_scheduler = comp_scheduler_cls.from_config(scheduler.config)
+                assert comp_scheduler is not None
+
+            new_scheduler = scheduler_class.from_config(comp_scheduler.config)
+
+            new_scheduler_config = {k: v for k, v in new_scheduler.config.items() if k in scheduler.config}
+            scheduler_diff = {k: v for k, v in new_scheduler.config.items() if k not in scheduler.config}
+
+            # make sure that configs are essentially identical
+            assert new_scheduler_config == dict(scheduler.config)
+
+            # make sure that only differences are for configs that are not in init
+            init_keys = inspect.signature(scheduler_class.__init__).parameters.keys()
+            assert set(scheduler_diff.keys()).intersection(set(init_keys)) == set()
+
+    def test_from_pretrained(self):
+        for scheduler_class in self.scheduler_classes:
+            scheduler_config = self.get_scheduler_config()
+
+            scheduler = scheduler_class(**scheduler_config)
+
+            with tempfile.TemporaryDirectory() as tmpdirname:
+                scheduler.save_pretrained(tmpdirname)
+                new_scheduler = scheduler_class.from_pretrained(tmpdirname)
+
+            assert scheduler.config == new_scheduler.config
+
+    def test_step_shape(self):
+        kwargs = dict(self.forward_default_kwargs)
+
+        num_inference_steps = kwargs.pop("num_inference_steps", None)
+
+        timestep_0 = 0
+        timestep_1 = 1
+
+        for scheduler_class in self.scheduler_classes:
+            if scheduler_class in (EulerAncestralDiscreteScheduler, EulerDiscreteScheduler, LMSDiscreteScheduler):
+                timestep_0 = float(timestep_0)
+                timestep_1 = float(timestep_1)
+
+            scheduler_config = self.get_scheduler_config()
+            scheduler = scheduler_class(**scheduler_config)
+
+            if scheduler_class == VQDiffusionScheduler:
+                num_vec_classes = scheduler_config["num_vec_classes"]
+                sample = self.dummy_sample(num_vec_classes)
+                model = self.dummy_model(num_vec_classes)
+                residual = model(sample, timestep_0)
+            else:
+                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(residual, timestep_0, sample, **kwargs).prev_sample
+            output_1 = scheduler.step(residual, timestep_1, sample, **kwargs).prev_sample
+
+            self.assertEqual(output_0.shape, sample.shape)
+            self.assertEqual(output_0.shape, output_1.shape)
+
+    def test_scheduler_outputs_equivalence(self):
+        def set_nan_tensor_to_zero(t):
+            t[t != t] = 0
+            return t
+
+        def recursive_check(tuple_object, dict_object):
+            if isinstance(tuple_object, (List, Tuple)):
+                for tuple_iterable_value, dict_iterable_value in zip(tuple_object, dict_object.values()):
+                    recursive_check(tuple_iterable_value, dict_iterable_value)
+            elif isinstance(tuple_object, Dict):
+                for tuple_iterable_value, dict_iterable_value in zip(tuple_object.values(), dict_object.values()):
+                    recursive_check(tuple_iterable_value, dict_iterable_value)
+            elif tuple_object is None:
+                return
+            else:
+                self.assertTrue(
+                    torch.allclose(
+                        set_nan_tensor_to_zero(tuple_object), set_nan_tensor_to_zero(dict_object), atol=1e-5
+                    ),
+                    msg=(
+                        "Tuple and dict output are not equal. Difference:"
+                        f" {torch.max(torch.abs(tuple_object - dict_object))}. Tuple has `nan`:"
+                        f" {torch.isnan(tuple_object).any()} and `inf`: {torch.isinf(tuple_object)}. Dict has"
+                        f" `nan`: {torch.isnan(dict_object).any()} and `inf`: {torch.isinf(dict_object)}."
+                    ),
+                )
+
+        kwargs = dict(self.forward_default_kwargs)
+        num_inference_steps = kwargs.pop("num_inference_steps", 50)
+
+        timestep = 0
+        if len(self.scheduler_classes) > 0 and self.scheduler_classes[0] == IPNDMScheduler:
+            timestep = 1
+
+        for scheduler_class in self.scheduler_classes:
+            if scheduler_class in (EulerAncestralDiscreteScheduler, EulerDiscreteScheduler, LMSDiscreteScheduler):
+                timestep = float(timestep)
+
+            scheduler_config = self.get_scheduler_config()
+            scheduler = scheduler_class(**scheduler_config)
+
+            if scheduler_class == VQDiffusionScheduler:
+                num_vec_classes = scheduler_config["num_vec_classes"]
+                sample = self.dummy_sample(num_vec_classes)
+                model = self.dummy_model(num_vec_classes)
+                residual = model(sample, timestep)
+            else:
+                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
+
+            # Set the seed before state as some schedulers are stochastic like EulerAncestralDiscreteScheduler, EulerDiscreteScheduler
+            if "generator" in set(inspect.signature(scheduler.step).parameters.keys()):
+                kwargs["generator"] = torch.manual_seed(0)
+            outputs_dict = scheduler.step(residual, timestep, sample, **kwargs)
+
+            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
+
+            # Set the seed before state as some schedulers are stochastic like EulerAncestralDiscreteScheduler, EulerDiscreteScheduler
+            if "generator" in set(inspect.signature(scheduler.step).parameters.keys()):
+                kwargs["generator"] = torch.manual_seed(0)
+            outputs_tuple = scheduler.step(residual, timestep, sample, return_dict=False, **kwargs)
+
+            recursive_check(outputs_tuple, outputs_dict)
+
+    def test_scheduler_public_api(self):
+        for scheduler_class in self.scheduler_classes:
+            scheduler_config = self.get_scheduler_config()
+            scheduler = scheduler_class(**scheduler_config)
+
+            if scheduler_class != VQDiffusionScheduler:
+                self.assertTrue(
+                    hasattr(scheduler, "init_noise_sigma"),
+                    f"{scheduler_class} does not implement a required attribute `init_noise_sigma`",
+                )
+                self.assertTrue(
+                    hasattr(scheduler, "scale_model_input"),
+                    (
+                        f"{scheduler_class} does not implement a required class method `scale_model_input(sample,"
+                        " timestep)`"
+                    ),
+                )
+            self.assertTrue(
+                hasattr(scheduler, "step"),
+                f"{scheduler_class} does not implement a required class method `step(...)`",
+            )
+
+            if scheduler_class != VQDiffusionScheduler:
+                sample = self.dummy_sample
+                scaled_sample = scheduler.scale_model_input(sample, 0.0)
+                self.assertEqual(sample.shape, scaled_sample.shape)
+
+    def test_add_noise_device(self):
+        for scheduler_class in self.scheduler_classes:
+            if scheduler_class == IPNDMScheduler:
+                continue
+            scheduler_config = self.get_scheduler_config()
+            scheduler = scheduler_class(**scheduler_config)
+            scheduler.set_timesteps(100)
+
+            sample = self.dummy_sample.to(torch_device)
+            scaled_sample = scheduler.scale_model_input(sample, 0.0)
+            self.assertEqual(sample.shape, scaled_sample.shape)
+
+            noise = torch.randn_like(scaled_sample).to(torch_device)
+            t = scheduler.timesteps[5][None]
+            noised = scheduler.add_noise(scaled_sample, noise, t)
+            self.assertEqual(noised.shape, scaled_sample.shape)
+
+    def test_deprecated_kwargs(self):
+        for scheduler_class in self.scheduler_classes:
+            has_kwarg_in_model_class = "kwargs" in inspect.signature(scheduler_class.__init__).parameters
+            has_deprecated_kwarg = len(scheduler_class._deprecated_kwargs) > 0
+
+            if has_kwarg_in_model_class and not has_deprecated_kwarg:
+                raise ValueError(
+                    f"{scheduler_class} has `**kwargs` in its __init__ method but has not defined any deprecated"
+                    " kwargs under the `_deprecated_kwargs` class attribute. Make sure to either remove `**kwargs` if"
+                    " there are no deprecated arguments or add the deprecated argument with `_deprecated_kwargs ="
+                    " [<deprecated_argument>]`"
+                )
+
+            if not has_kwarg_in_model_class and has_deprecated_kwarg:
+                raise ValueError(
+                    f"{scheduler_class} doesn't have `**kwargs` in its __init__ method but has defined deprecated"
+                    " kwargs under the `_deprecated_kwargs` class attribute. Make sure to either add the `**kwargs`"
+                    f" argument to {self.model_class}.__init__ if there are deprecated arguments or remove the"
+                    " deprecated argument from `_deprecated_kwargs = [<deprecated_argument>]`"
+                )
+
+    def test_trained_betas(self):
+        for scheduler_class in self.scheduler_classes:
+            if scheduler_class == VQDiffusionScheduler:
+                continue
+
+            scheduler_config = self.get_scheduler_config()
+            scheduler = scheduler_class(**scheduler_config, trained_betas=np.array([0.1, 0.3]))
+
+            with tempfile.TemporaryDirectory() as tmpdirname:
+                scheduler.save_pretrained(tmpdirname)
+                new_scheduler = scheduler_class.from_pretrained(tmpdirname)
+
+            assert scheduler.betas.tolist() == new_scheduler.betas.tolist()