DPM++ third order fixes (#9104)

* Fix wrong output on 3n-1 steps count

* Add sde handling to 3 order

* make

* copies

---------
Co-authored-by: hlky <hlky@hlky.ac>

src/diffusers/__init__.py

@@ -338,8 +338,8 @@ else:
             "StableDiffusion3ControlNetPipeline",
             "StableDiffusion3Img2ImgPipeline",
             "StableDiffusion3InpaintPipeline",
-            "StableDiffusion3PAGPipeline",
             "StableDiffusion3PAGImg2ImgPipeline",
+            "StableDiffusion3PAGPipeline",
             "StableDiffusion3Pipeline",
             "StableDiffusionAdapterPipeline",
             "StableDiffusionAttendAndExcitePipeline",

src/diffusers/schedulers/scheduling_dpmsolver_multistep.py

@@ -889,6 +889,7 @@ class DPMSolverMultistepScheduler(SchedulerMixin, ConfigMixin):
         model_output_list: List[torch.Tensor],
         *args,
         sample: torch.Tensor = None,
+        noise: Optional[torch.Tensor] = None,
         **kwargs,
     ) -> torch.Tensor:
         """
@@ -967,6 +968,15 @@ class DPMSolverMultistepScheduler(SchedulerMixin, ConfigMixin):
                 - (sigma_t * ((torch.exp(h) - 1.0) / h - 1.0)) * D1
                 - (sigma_t * ((torch.exp(h) - 1.0 - h) / h**2 - 0.5)) * D2
             )
+        elif self.config.algorithm_type == "sde-dpmsolver++":
+            assert noise is not None
+            x_t = (
+                (sigma_t / sigma_s0 * torch.exp(-h)) * sample
+                + (alpha_t * (1.0 - torch.exp(-2.0 * h))) * D0
+                + (alpha_t * ((1.0 - torch.exp(-2.0 * h)) / (-2.0 * h) + 1.0)) * D1
+                + (alpha_t * ((1.0 - torch.exp(-2.0 * h) - 2.0 * h) / (2.0 * h) ** 2 - 0.5)) * D2
+                + sigma_t * torch.sqrt(1.0 - torch.exp(-2 * h)) * noise
+            )
         return x_t
 
     def index_for_timestep(self, timestep, schedule_timesteps=None):
@@ -1073,7 +1083,7 @@ class DPMSolverMultistepScheduler(SchedulerMixin, ConfigMixin):
         elif self.config.solver_order == 2 or self.lower_order_nums < 2 or lower_order_second:
             prev_sample = self.multistep_dpm_solver_second_order_update(self.model_outputs, sample=sample, noise=noise)
         else:
-            prev_sample = self.multistep_dpm_solver_third_order_update(self.model_outputs, sample=sample)
+            prev_sample = self.multistep_dpm_solver_third_order_update(self.model_outputs, sample=sample, noise=noise)
 
         if self.lower_order_nums < self.config.solver_order:
             self.lower_order_nums += 1

src/diffusers/schedulers/scheduling_dpmsolver_multistep_inverse.py

@@ -764,6 +764,7 @@ class DPMSolverMultistepInverseScheduler(SchedulerMixin, ConfigMixin):
         model_output_list: List[torch.Tensor],
         *args,
         sample: torch.Tensor = None,
+        noise: Optional[torch.Tensor] = None,
         **kwargs,
     ) -> torch.Tensor:
         """
@@ -842,6 +843,15 @@ class DPMSolverMultistepInverseScheduler(SchedulerMixin, ConfigMixin):
                 - (sigma_t * ((torch.exp(h) - 1.0) / h - 1.0)) * D1
                 - (sigma_t * ((torch.exp(h) - 1.0 - h) / h**2 - 0.5)) * D2
             )
+        elif self.config.algorithm_type == "sde-dpmsolver++":
+            assert noise is not None
+            x_t = (
+                (sigma_t / sigma_s0 * torch.exp(-h)) * sample
+                + (alpha_t * (1.0 - torch.exp(-2.0 * h))) * D0
+                + (alpha_t * ((1.0 - torch.exp(-2.0 * h)) / (-2.0 * h) + 1.0)) * D1
+                + (alpha_t * ((1.0 - torch.exp(-2.0 * h) - 2.0 * h) / (2.0 * h) ** 2 - 0.5)) * D2
+                + sigma_t * torch.sqrt(1.0 - torch.exp(-2 * h)) * noise
+            )
         return x_t
 
     def _init_step_index(self, timestep):

src/diffusers/schedulers/scheduling_dpmsolver_singlestep.py

@@ -264,6 +264,10 @@ class DPMSolverSinglestepScheduler(SchedulerMixin, ConfigMixin):
                 orders = [1, 2] * (steps // 2)
             elif order == 1:
                 orders = [1] * steps
+
+        if self.config.final_sigmas_type == "zero":
+            orders[-1] = 1
+
         return orders
 
     @property
@@ -812,6 +816,7 @@ class DPMSolverSinglestepScheduler(SchedulerMixin, ConfigMixin):
         model_output_list: List[torch.Tensor],
         *args,
         sample: torch.Tensor = None,
+        noise: Optional[torch.Tensor] = None,
         **kwargs,
     ) -> torch.Tensor:
         """
@@ -909,6 +914,23 @@ class DPMSolverSinglestepScheduler(SchedulerMixin, ConfigMixin):
                     - (sigma_t * ((torch.exp(h) - 1.0) / h - 1.0)) * D1
                     - (sigma_t * ((torch.exp(h) - 1.0 - h) / h**2 - 0.5)) * D2
                 )
+        elif self.config.algorithm_type == "sde-dpmsolver++":
+            assert noise is not None
+            if self.config.solver_type == "midpoint":
+                x_t = (
+                    (sigma_t / sigma_s2 * torch.exp(-h)) * sample
+                    + (alpha_t * (1.0 - torch.exp(-2.0 * h))) * D0
+                    + (alpha_t * ((1.0 - torch.exp(-2.0 * h)) / (-2.0 * h) + 1.0)) * D1_1
+                    + sigma_t * torch.sqrt(1.0 - torch.exp(-2 * h)) * noise
+                )
+            elif self.config.solver_type == "heun":
+                x_t = (
+                    (sigma_t / sigma_s2 * torch.exp(-h)) * sample
+                    + (alpha_t * (1.0 - torch.exp(-2.0 * h))) * D0
+                    + (alpha_t * ((1.0 - torch.exp(-2.0 * h)) / (-2.0 * h) + 1.0)) * D1
+                    + (alpha_t * ((1.0 - torch.exp(-2.0 * h) + (-2.0 * h)) / (-2.0 * h) ** 2 - 0.5)) * D2
+                    + sigma_t * torch.sqrt(1.0 - torch.exp(-2 * h)) * noise
+                )
         return x_t
 
     def singlestep_dpm_solver_update(
@@ -970,7 +992,7 @@ class DPMSolverSinglestepScheduler(SchedulerMixin, ConfigMixin):
         elif order == 2:
             return self.singlestep_dpm_solver_second_order_update(model_output_list, sample=sample, noise=noise)
         elif order == 3:
-            return self.singlestep_dpm_solver_third_order_update(model_output_list, sample=sample)
+            return self.singlestep_dpm_solver_third_order_update(model_output_list, sample=sample, noise=noise)
         else:
             raise ValueError(f"Order must be 1, 2, 3, got {order}")