feat : add log-rho deis multistep scheduler (#1432)

* feat : add log-rho deis multistep deis

* docs :fix typo

* docs : add docs for impl algo

* docs : remove duplicate ref

* finish deis

* add docs

* fix
Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>

docs/source/en/_toctree.yml

@@ -155,6 +155,8 @@
       title: "DDIM"
     - local: api/schedulers/ddpm
       title: "DDPM"
+    - local: api/schedulers/deis
+      title: "DEIS"
     - local: api/schedulers/singlestep_dpm_solver
       title: "Singlestep DPM-Solver"
     - local: api/schedulers/multistep_dpm_solver

docs/source/en/api/schedulers/deis.mdx

+<!--Copyright 2022 The HuggingFace Team. All rights reserved.
+
+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.
+-->
+
+# DEIS
+
+Fast Sampling of Diffusion Models with Exponential Integrator.
+
+## Overview
+
+Original paper can be found [here](https://arxiv.org/abs/2204.13902). The original implementation can be found [here](https://github.com/qsh-zh/deis).
+
+## DEISMultistepScheduler
+[[autodoc]] DEISMultistepScheduler

src/diffusers/__init__.py

@@ -67,6 +67,7 @@ else:
     from .schedulers import (
         DDIMScheduler,
         DDPMScheduler,
+        DEISMultistepScheduler,
         DPMSolverMultistepScheduler,
         DPMSolverSinglestepScheduler,
         EulerAncestralDiscreteScheduler,

src/diffusers/schedulers/__init__.py

@@ -24,6 +24,7 @@ except OptionalDependencyNotAvailable:
 else:
     from .scheduling_ddim import DDIMScheduler
     from .scheduling_ddpm import DDPMScheduler
+    from .scheduling_deis_multistep import DEISMultistepScheduler
     from .scheduling_dpmsolver_multistep import DPMSolverMultistepScheduler
     from .scheduling_dpmsolver_singlestep import DPMSolverSinglestepScheduler
     from .scheduling_euler_ancestral_discrete import EulerAncestralDiscreteScheduler

src/diffusers/schedulers/scheduling_dpmsolver_multistep.py

@@ -174,9 +174,15 @@ class DPMSolverMultistepScheduler(SchedulerMixin, ConfigMixin):
 
         # settings for DPM-Solver
         if algorithm_type not in ["dpmsolver", "dpmsolver++"]:
-            raise NotImplementedError(f"{algorithm_type} does is not implemented for {self.__class__}")
+            if algorithm_type == "deis":
+                algorithm_type = "dpmsolver++"
+            else:
+                raise NotImplementedError(f"{algorithm_type} does is not implemented for {self.__class__}")
         if solver_type not in ["midpoint", "heun"]:
-            raise NotImplementedError(f"{solver_type} does is not implemented for {self.__class__}")
+            if solver_type == "logrho":
+                solver_type = "midpoint"
+            else:
+                raise NotImplementedError(f"{solver_type} does is not implemented for {self.__class__}")
 
         # setable values
         self.num_inference_steps = None

src/diffusers/schedulers/scheduling_dpmsolver_singlestep.py

@@ -163,9 +163,15 @@ class DPMSolverSinglestepScheduler(SchedulerMixin, ConfigMixin):
 
         # settings for DPM-Solver
         if algorithm_type not in ["dpmsolver", "dpmsolver++"]:
-            raise NotImplementedError(f"{algorithm_type} does is not implemented for {self.__class__}")
+            if algorithm_type == "deis":
+                algorithm_type = "dpmsolver++"
+            else:
+                raise NotImplementedError(f"{algorithm_type} does is not implemented for {self.__class__}")
         if solver_type not in ["midpoint", "heun"]:
-            raise NotImplementedError(f"{solver_type} does is not implemented for {self.__class__}")
+            if solver_type == "logrho":
+                solver_type = "midpoint"
+            else:
+                raise NotImplementedError(f"{solver_type} does is not implemented for {self.__class__}")
 
         # setable values
         self.num_inference_steps = None

src/diffusers/utils/constants.py

@@ -41,4 +41,7 @@ _COMPATIBLE_STABLE_DIFFUSION_SCHEDULERS = [
     "EulerAncestralDiscreteScheduler",
     "DPMSolverMultistepScheduler",
     "DPMSolverSinglestepScheduler",
+    "KDPM2DiscreteScheduler",
+    "KDPM2AncestralDiscreteScheduler",
+    "DEISMultistepScheduler",
 ]

src/diffusers/utils/dummy_pt_objects.py

@@ -362,6 +362,21 @@ class DDPMScheduler(metaclass=DummyObject):
         requires_backends(cls, ["torch"])
 
 
+class DEISMultistepScheduler(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 DPMSolverMultistepScheduler(metaclass=DummyObject):
     _backends = ["torch"]
 

tests/test_scheduler.py

@@ -27,6 +27,7 @@ import diffusers
 from diffusers import (
     DDIMScheduler,
     DDPMScheduler,
+    DEISMultistepScheduler,
     DPMSolverMultistepScheduler,
     DPMSolverSinglestepScheduler,
     EulerAncestralDiscreteScheduler,
@@ -2505,6 +2506,207 @@ class KDPM2DiscreteSchedulerTest(SchedulerCommonTest):
             assert abs(result_mean.item() - 0.0266) < 1e-3
 
 
+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, **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_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
+
+
 class KDPM2AncestralDiscreteSchedulerTest(SchedulerCommonTest):
     scheduler_classes = (KDPM2AncestralDiscreteScheduler,)
     num_inference_steps = 10