copies

src/diffusers/schedulers/scheduling_deis_multistep.py

@@ -734,7 +734,16 @@ class DEISMultistepScheduler(SchedulerMixin, ConfigMixin):
             schedule_timesteps = self.timesteps.to(original_samples.device)
             timesteps = timesteps.to(original_samples.device)
 
-        step_indices = [(schedule_timesteps == t).nonzero().item() for t in timesteps]
+        step_indices = []
+        for timestep in timesteps:
+            index_candidates = (schedule_timesteps == timestep).nonzero()
+            if len(index_candidates) == 0:
+                step_index = len(schedule_timesteps) - 1
+            elif len(index_candidates) > 1:
+                step_index = index_candidates[1].item()
+            else:
+                step_index = index_candidates[0].item()
+            step_indices.append(step_index)
 
         sigma = sigmas[step_indices].flatten()
         while len(sigma.shape) < len(original_samples.shape):

src/diffusers/schedulers/scheduling_dpmsolver_multistep.py

@@ -896,7 +896,16 @@ class DPMSolverMultistepScheduler(SchedulerMixin, ConfigMixin):
             schedule_timesteps = self.timesteps.to(original_samples.device)
             timesteps = timesteps.to(original_samples.device)
 
-        step_indices = [(schedule_timesteps == t).nonzero().item() for t in timesteps]
+        step_indices = []
+        for timestep in timesteps:
+            index_candidates = (schedule_timesteps == timestep).nonzero()
+            if len(index_candidates) == 0:
+                step_index = len(schedule_timesteps) - 1
+            elif len(index_candidates) > 1:
+                step_index = index_candidates[1].item()
+            else:
+                step_index = index_candidates[0].item()
+            step_indices.append(step_index)
 
         sigma = sigmas[step_indices].flatten()
         while len(sigma.shape) < len(original_samples.shape):

src/diffusers/schedulers/scheduling_dpmsolver_multistep_inverse.py

@@ -891,7 +891,16 @@ class DPMSolverMultistepInverseScheduler(SchedulerMixin, ConfigMixin):
             schedule_timesteps = self.timesteps.to(original_samples.device)
             timesteps = timesteps.to(original_samples.device)
 
-        step_indices = [(schedule_timesteps == t).nonzero().item() for t in timesteps]
+        step_indices = []
+        for timestep in timesteps:
+            index_candidates = (schedule_timesteps == timestep).nonzero()
+            if len(index_candidates) == 0:
+                step_index = len(schedule_timesteps) - 1
+            elif len(index_candidates) > 1:
+                step_index = index_candidates[1].item()
+            else:
+                step_index = index_candidates[0].item()
+            step_indices.append(step_index)
 
         sigma = sigmas[step_indices].flatten()
         while len(sigma.shape) < len(original_samples.shape):

src/diffusers/schedulers/scheduling_dpmsolver_singlestep.py

@@ -897,7 +897,16 @@ class DPMSolverSinglestepScheduler(SchedulerMixin, ConfigMixin):
             schedule_timesteps = self.timesteps.to(original_samples.device)
             timesteps = timesteps.to(original_samples.device)
 
-        step_indices = [(schedule_timesteps == t).nonzero().item() for t in timesteps]
+        step_indices = []
+        for timestep in timesteps:
+            index_candidates = (schedule_timesteps == timestep).nonzero()
+            if len(index_candidates) == 0:
+                step_index = len(schedule_timesteps) - 1
+            elif len(index_candidates) > 1:
+                step_index = index_candidates[1].item()
+            else:
+                step_index = index_candidates[0].item()
+            step_indices.append(step_index)
 
         sigma = sigmas[step_indices].flatten()
         while len(sigma.shape) < len(original_samples.shape):

src/diffusers/schedulers/scheduling_unipc_multistep.py

@@ -828,7 +828,16 @@ class UniPCMultistepScheduler(SchedulerMixin, ConfigMixin):
             schedule_timesteps = self.timesteps.to(original_samples.device)
             timesteps = timesteps.to(original_samples.device)
 
-        step_indices = [(schedule_timesteps == t).nonzero().item() for t in timesteps]
+        step_indices = []
+        for timestep in timesteps:
+            index_candidates = (schedule_timesteps == timestep).nonzero()
+            if len(index_candidates) == 0:
+                step_index = len(schedule_timesteps) - 1
+            elif len(index_candidates) > 1:
+                step_index = index_candidates[1].item()
+            else:
+                step_index = index_candidates[0].item()
+            step_indices.append(step_index)
 
         sigma = sigmas[step_indices].flatten()
         while len(sigma.shape) < len(original_samples.shape):