[Bug fix] "previous_timestep()" in DDPM scheduling compatible with "trailing"...

[Bug fix] "previous_timestep()" in DDPM scheduling compatible with "trailing" and "linspace" options (#9384)

* Update scheduling_ddpm.py

* fix copies

---------
Co-authored-by: YiYi Xu <yixu310@gmail.com>
Co-authored-by: hlky <hlky@hlky.ac>

src/diffusers/schedulers/scheduling_ddpm.py

@@ -548,16 +548,12 @@ class DDPMScheduler(SchedulerMixin, ConfigMixin):
         return self.config.num_train_timesteps
 
     def previous_timestep(self, timestep):
-        if self.custom_timesteps:
+        if self.custom_timesteps or self.num_inference_steps:
             index = (self.timesteps == timestep).nonzero(as_tuple=True)[0][0]
             if index == self.timesteps.shape[0] - 1:
                 prev_t = torch.tensor(-1)
             else:
                 prev_t = self.timesteps[index + 1]
         else:
-            num_inference_steps = (
-                self.num_inference_steps if self.num_inference_steps else self.config.num_train_timesteps
-            )
-            prev_t = timestep - self.config.num_train_timesteps // num_inference_steps
-
+            prev_t = timestep - 1
         return prev_t

src/diffusers/schedulers/scheduling_ddpm_parallel.py

@@ -639,16 +639,12 @@ class DDPMParallelScheduler(SchedulerMixin, ConfigMixin):
 
     # Copied from diffusers.schedulers.scheduling_ddpm.DDPMScheduler.previous_timestep
     def previous_timestep(self, timestep):
-        if self.custom_timesteps:
+        if self.custom_timesteps or self.num_inference_steps:
             index = (self.timesteps == timestep).nonzero(as_tuple=True)[0][0]
             if index == self.timesteps.shape[0] - 1:
                 prev_t = torch.tensor(-1)
             else:
                 prev_t = self.timesteps[index + 1]
         else:
-            num_inference_steps = (
-                self.num_inference_steps if self.num_inference_steps else self.config.num_train_timesteps
-            )
-            prev_t = timestep - self.config.num_train_timesteps // num_inference_steps
-
+            prev_t = timestep - 1
         return prev_t

src/diffusers/schedulers/scheduling_lcm.py

@@ -643,16 +643,12 @@ class LCMScheduler(SchedulerMixin, ConfigMixin):
 
     # Copied from diffusers.schedulers.scheduling_ddpm.DDPMScheduler.previous_timestep
     def previous_timestep(self, timestep):
-        if self.custom_timesteps:
+        if self.custom_timesteps or self.num_inference_steps:
             index = (self.timesteps == timestep).nonzero(as_tuple=True)[0][0]
             if index == self.timesteps.shape[0] - 1:
                 prev_t = torch.tensor(-1)
             else:
                 prev_t = self.timesteps[index + 1]
         else:
-            num_inference_steps = (
-                self.num_inference_steps if self.num_inference_steps else self.config.num_train_timesteps
-            )
-            prev_t = timestep - self.config.num_train_timesteps // num_inference_steps
-
+            prev_t = timestep - 1
         return prev_t

src/diffusers/schedulers/scheduling_tcd.py

@@ -680,16 +680,12 @@ class TCDScheduler(SchedulerMixin, ConfigMixin):
 
     # Copied from diffusers.schedulers.scheduling_ddpm.DDPMScheduler.previous_timestep
     def previous_timestep(self, timestep):
-        if self.custom_timesteps:
+        if self.custom_timesteps or self.num_inference_steps:
             index = (self.timesteps == timestep).nonzero(as_tuple=True)[0][0]
             if index == self.timesteps.shape[0] - 1:
                 prev_t = torch.tensor(-1)
             else:
                 prev_t = self.timesteps[index + 1]
         else:
-            num_inference_steps = (
-                self.num_inference_steps if self.num_inference_steps else self.config.num_train_timesteps
-            )
-            prev_t = timestep - self.config.num_train_timesteps // num_inference_steps
-
+            prev_t = timestep - 1
         return prev_t