add Sa-Solver (#5975)

* add Sa-Solver



---------
Co-authored-by: Sayak Paul <spsayakpaul@gmail.com>
Co-authored-by: scxue <xueshuchen17@mails.ucas.edu.cn>
Co-authored-by: jschen <chenjunsong4@h-partners.com>
Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>
Co-authored-by: yiyixuxu <yixu310@gmail,com>

src/diffusers/__init__.py

@@ -153,6 +153,7 @@ else:
             "LCMScheduler",
             "PNDMScheduler",
             "RePaintScheduler",
+            "SASolverScheduler",
             "SchedulerMixin",
             "ScoreSdeVeScheduler",
             "UnCLIPScheduler",
@@ -530,6 +531,7 @@ if TYPE_CHECKING or DIFFUSERS_SLOW_IMPORT:
             LCMScheduler,
             PNDMScheduler,
             RePaintScheduler,
+            SASolverScheduler,
             SchedulerMixin,
             ScoreSdeVeScheduler,
             UnCLIPScheduler,

src/diffusers/schedulers/__init__.py

@@ -61,6 +61,7 @@ else:
     _import_structure["scheduling_lcm"] = ["LCMScheduler"]
     _import_structure["scheduling_pndm"] = ["PNDMScheduler"]
     _import_structure["scheduling_repaint"] = ["RePaintScheduler"]
+    _import_structure["scheduling_sasolver"] = ["SASolverScheduler"]
     _import_structure["scheduling_sde_ve"] = ["ScoreSdeVeScheduler"]
     _import_structure["scheduling_unclip"] = ["UnCLIPScheduler"]
     _import_structure["scheduling_unipc_multistep"] = ["UniPCMultistepScheduler"]
@@ -152,6 +153,7 @@ if TYPE_CHECKING or DIFFUSERS_SLOW_IMPORT:
         from .scheduling_lcm import LCMScheduler
         from .scheduling_pndm import PNDMScheduler
         from .scheduling_repaint import RePaintScheduler
+        from .scheduling_sasolver import SASolverScheduler
         from .scheduling_sde_ve import ScoreSdeVeScheduler
         from .scheduling_unclip import UnCLIPScheduler
         from .scheduling_unipc_multistep import UniPCMultistepScheduler

src/diffusers/utils/dummy_pt_objects.py

@@ -990,6 +990,21 @@ class RePaintScheduler(metaclass=DummyObject):
         requires_backends(cls, ["torch"])
 
 
+class SASolverScheduler(metaclass=DummyObject):
+    _backends = ["torch"]
+
+    def __init__(self, *args, **kwargs):
+        requires_backends(self, ["torch"])
+
+    @classmethod
+    def from_config(cls, *args, **kwargs):
+        requires_backends(cls, ["torch"])
+
+    @classmethod
+    def from_pretrained(cls, *args, **kwargs):
+        requires_backends(cls, ["torch"])
+
+
 class SchedulerMixin(metaclass=DummyObject):
     _backends = ["torch"]
 

tests/schedulers/test_scheduler_sasolver.py

+import torch
+
+from diffusers import SASolverScheduler
+from diffusers.utils.testing_utils import require_torchsde, torch_device
+
+from .test_schedulers import SchedulerCommonTest
+
+
+@require_torchsde
+class SASolverSchedulerTest(SchedulerCommonTest):
+    scheduler_classes = (SASolverScheduler,)
+    forward_default_kwargs = (("num_inference_steps", 10),)
+    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_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[
+                : max(
+                    scheduler.config.predictor_order,
+                    scheduler.config.corrector_order - 1,
+                )
+            ]
+
+            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 [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)
+        generator = torch.manual_seed(0)
+
+        for i, t in enumerate(scheduler.timesteps):
+            sample = scheduler.scale_model_input(sample, t, generator=generator)
+
+            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"]:
+            assert abs(result_sum.item() - 337.394287109375) < 1e-2
+            assert abs(result_mean.item() - 0.43931546807289124) < 1e-3
+        elif torch_device in ["cuda"]:
+            assert abs(result_sum.item() - 329.1999816894531) < 1e-2
+            assert abs(result_mean.item() - 0.4286458194255829) < 1e-3
+        else:
+            print("None")
+
+    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)
+        generator = torch.manual_seed(0)
+
+        for i, t in enumerate(scheduler.timesteps):
+            sample = scheduler.scale_model_input(sample, t, generator=generator)
+
+            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"]:
+            assert abs(result_sum.item() - 193.1467742919922) < 1e-2
+            assert abs(result_mean.item() - 0.2514931857585907) < 1e-3
+        elif torch_device in ["cuda"]:
+            assert abs(result_sum.item() - 193.4154052734375) < 1e-2
+            assert abs(result_mean.item() - 0.2518429756164551) < 1e-3
+        else:
+            print("None")
+
+    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
+        generator = torch.manual_seed(0)
+
+        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))
+
+        if torch_device in ["cpu"]:
+            assert abs(result_sum.item() - 337.394287109375) < 1e-2
+            assert abs(result_mean.item() - 0.43931546807289124) < 1e-3
+        elif torch_device in ["cuda"]:
+            assert abs(result_sum.item() - 337.394287109375) < 1e-2
+            assert abs(result_mean.item() - 0.4393154978752136) < 1e-3
+        else:
+            print("None")
+
+    def test_full_loop_device_karras_sigmas(self):
+        scheduler_class = self.scheduler_classes[0]
+        scheduler_config = self.get_scheduler_config()
+        scheduler = scheduler_class(**scheduler_config, use_karras_sigmas=True)
+
+        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
+        sample = sample.to(torch_device)
+        generator = torch.manual_seed(0)
+
+        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))
+
+        if torch_device in ["cpu"]:
+            assert abs(result_sum.item() - 837.2554931640625) < 1e-2
+            assert abs(result_mean.item() - 1.0901764631271362) < 1e-2
+        elif torch_device in ["cuda"]:
+            assert abs(result_sum.item() - 837.25537109375) < 1e-2
+            assert abs(result_mean.item() - 1.0901763439178467) < 1e-2
+        else:
+            print("None")