diff --git a/docs/source/en/api/pipelines/kolors.md b/docs/source/en/api/pipelines/kolors.md
index dce277942855..367eb4a48548 100644
--- a/docs/source/en/api/pipelines/kolors.md
+++ b/docs/source/en/api/pipelines/kolors.md
@@ -105,3 +105,11 @@ image.save("kolors_ipa_sample.png")
 
 - all
 - __call__
+
+## KolorsImg2ImgPipeline
+
+[[autodoc]] KolorsImg2ImgPipeline
+
+- all
+- __call__
+
diff --git a/src/diffusers/pipelines/controlnet/pipeline_controlnet_inpaint_sd_xl.py b/src/diffusers/pipelines/controlnet/pipeline_controlnet_inpaint_sd_xl.py
index 017c1a6f7434..17fd2cb6c81d 100644
--- a/src/diffusers/pipelines/controlnet/pipeline_controlnet_inpaint_sd_xl.py
+++ b/src/diffusers/pipelines/controlnet/pipeline_controlnet_inpaint_sd_xl.py
@@ -1024,14 +1024,16 @@ def get_timesteps(self, num_inference_steps, strength, device, denoising_start=N
         if denoising_start is None:
             init_timestep = min(int(num_inference_steps * strength), num_inference_steps)
             t_start = max(num_inference_steps - init_timestep, 0)
-        else:
-            t_start = 0
 
-        timesteps = self.scheduler.timesteps[t_start * self.scheduler.order :]
+            timesteps = self.scheduler.timesteps[t_start * self.scheduler.order :]
+            if hasattr(self.scheduler, "set_begin_index"):
+                self.scheduler.set_begin_index(t_start * self.scheduler.order)
+
+            return timesteps, num_inference_steps - t_start
 
-        # Strength is irrelevant if we directly request a timestep to start at;
-        # that is, strength is determined by the denoising_start instead.
-        if denoising_start is not None:
+        else:
+            # Strength is irrelevant if we directly request a timestep to start at;
+            # that is, strength is determined by the denoising_start instead.
             discrete_timestep_cutoff = int(
                 round(
                     self.scheduler.config.num_train_timesteps
@@ -1039,7 +1041,7 @@ def get_timesteps(self, num_inference_steps, strength, device, denoising_start=N
                 )
             )
 
-            num_inference_steps = (timesteps < discrete_timestep_cutoff).sum().item()
+            num_inference_steps = (self.scheduler.timesteps < discrete_timestep_cutoff).sum().item()
             if self.scheduler.order == 2 and num_inference_steps % 2 == 0:
                 # if the scheduler is a 2nd order scheduler we might have to do +1
                 # because `num_inference_steps` might be even given that every timestep
@@ -1050,11 +1052,12 @@ def get_timesteps(self, num_inference_steps, strength, device, denoising_start=N
                 num_inference_steps = num_inference_steps + 1
 
             # because t_n+1 >= t_n, we slice the timesteps starting from the end
-            timesteps = timesteps[-num_inference_steps:]
+            t_start = len(self.scheduler.timesteps) - num_inference_steps
+            timesteps = self.scheduler.timesteps[t_start:]
+            if hasattr(self.scheduler, "set_begin_index"):
+                self.scheduler.set_begin_index(t_start)
             return timesteps, num_inference_steps
 
-        return timesteps, num_inference_steps - t_start
-
     def _get_add_time_ids(
         self,
         original_size,
diff --git a/src/diffusers/pipelines/kolors/pipeline_kolors_img2img.py b/src/diffusers/pipelines/kolors/pipeline_kolors_img2img.py
index 81abdff0e9cc..4985a80f88df 100644
--- a/src/diffusers/pipelines/kolors/pipeline_kolors_img2img.py
+++ b/src/diffusers/pipelines/kolors/pipeline_kolors_img2img.py
@@ -564,14 +564,16 @@ def get_timesteps(self, num_inference_steps, strength, device, denoising_start=N
         if denoising_start is None:
             init_timestep = min(int(num_inference_steps * strength), num_inference_steps)
             t_start = max(num_inference_steps - init_timestep, 0)
-        else:
-            t_start = 0
 
-        timesteps = self.scheduler.timesteps[t_start * self.scheduler.order :]
+            timesteps = self.scheduler.timesteps[t_start * self.scheduler.order :]
+            if hasattr(self.scheduler, "set_begin_index"):
+                self.scheduler.set_begin_index(t_start * self.scheduler.order)
+
+            return timesteps, num_inference_steps - t_start
 
-        # Strength is irrelevant if we directly request a timestep to start at;
-        # that is, strength is determined by the denoising_start instead.
-        if denoising_start is not None:
+        else:
+            # Strength is irrelevant if we directly request a timestep to start at;
+            # that is, strength is determined by the denoising_start instead.
             discrete_timestep_cutoff = int(
                 round(
                     self.scheduler.config.num_train_timesteps
@@ -579,7 +581,7 @@ def get_timesteps(self, num_inference_steps, strength, device, denoising_start=N
                 )
             )
 
-            num_inference_steps = (timesteps < discrete_timestep_cutoff).sum().item()
+            num_inference_steps = (self.scheduler.timesteps < discrete_timestep_cutoff).sum().item()
             if self.scheduler.order == 2 and num_inference_steps % 2 == 0:
                 # if the scheduler is a 2nd order scheduler we might have to do +1
                 # because `num_inference_steps` might be even given that every timestep
@@ -590,11 +592,12 @@ def get_timesteps(self, num_inference_steps, strength, device, denoising_start=N
                 num_inference_steps = num_inference_steps + 1
 
             # because t_n+1 >= t_n, we slice the timesteps starting from the end
-            timesteps = timesteps[-num_inference_steps:]
+            t_start = len(self.scheduler.timesteps) - num_inference_steps
+            timesteps = self.scheduler.timesteps[t_start:]
+            if hasattr(self.scheduler, "set_begin_index"):
+                self.scheduler.set_begin_index(t_start)
             return timesteps, num_inference_steps
 
-        return timesteps, num_inference_steps - t_start
-
     # Copied from diffusers.pipelines.stable_diffusion_xl.pipeline_stable_diffusion_xl_img2img.StableDiffusionXLImg2ImgPipeline.prepare_latents
     def prepare_latents(
         self, image, timestep, batch_size, num_images_per_prompt, dtype, device, generator=None, add_noise=True
diff --git a/src/diffusers/pipelines/pag/pipeline_pag_sd_xl_img2img.py b/src/diffusers/pipelines/pag/pipeline_pag_sd_xl_img2img.py
index 2ce81f6765e1..dc85aaaca37f 100644
--- a/src/diffusers/pipelines/pag/pipeline_pag_sd_xl_img2img.py
+++ b/src/diffusers/pipelines/pag/pipeline_pag_sd_xl_img2img.py
@@ -648,14 +648,16 @@ def get_timesteps(self, num_inference_steps, strength, device, denoising_start=N
         if denoising_start is None:
             init_timestep = min(int(num_inference_steps * strength), num_inference_steps)
             t_start = max(num_inference_steps - init_timestep, 0)
-        else:
-            t_start = 0
 
-        timesteps = self.scheduler.timesteps[t_start * self.scheduler.order :]
+            timesteps = self.scheduler.timesteps[t_start * self.scheduler.order :]
+            if hasattr(self.scheduler, "set_begin_index"):
+                self.scheduler.set_begin_index(t_start * self.scheduler.order)
+
+            return timesteps, num_inference_steps - t_start
 
-        # Strength is irrelevant if we directly request a timestep to start at;
-        # that is, strength is determined by the denoising_start instead.
-        if denoising_start is not None:
+        else:
+            # Strength is irrelevant if we directly request a timestep to start at;
+            # that is, strength is determined by the denoising_start instead.
             discrete_timestep_cutoff = int(
                 round(
                     self.scheduler.config.num_train_timesteps
@@ -663,7 +665,7 @@ def get_timesteps(self, num_inference_steps, strength, device, denoising_start=N
                 )
             )
 
-            num_inference_steps = (timesteps < discrete_timestep_cutoff).sum().item()
+            num_inference_steps = (self.scheduler.timesteps < discrete_timestep_cutoff).sum().item()
             if self.scheduler.order == 2 and num_inference_steps % 2 == 0:
                 # if the scheduler is a 2nd order scheduler we might have to do +1
                 # because `num_inference_steps` might be even given that every timestep
@@ -674,11 +676,12 @@ def get_timesteps(self, num_inference_steps, strength, device, denoising_start=N
                 num_inference_steps = num_inference_steps + 1
 
             # because t_n+1 >= t_n, we slice the timesteps starting from the end
-            timesteps = timesteps[-num_inference_steps:]
+            t_start = len(self.scheduler.timesteps) - num_inference_steps
+            timesteps = self.scheduler.timesteps[t_start:]
+            if hasattr(self.scheduler, "set_begin_index"):
+                self.scheduler.set_begin_index(t_start)
             return timesteps, num_inference_steps
 
-        return timesteps, num_inference_steps - t_start
-
     # Copied from diffusers.pipelines.stable_diffusion_xl.pipeline_stable_diffusion_xl_img2img.StableDiffusionXLImg2ImgPipeline.prepare_latents
     def prepare_latents(
         self, image, timestep, batch_size, num_images_per_prompt, dtype, device, generator=None, add_noise=True
diff --git a/src/diffusers/pipelines/pag/pipeline_pag_sd_xl_inpaint.py b/src/diffusers/pipelines/pag/pipeline_pag_sd_xl_inpaint.py
index 09c3a7029ceb..f5ebf4300934 100644
--- a/src/diffusers/pipelines/pag/pipeline_pag_sd_xl_inpaint.py
+++ b/src/diffusers/pipelines/pag/pipeline_pag_sd_xl_inpaint.py
@@ -897,14 +897,16 @@ def get_timesteps(self, num_inference_steps, strength, device, denoising_start=N
         if denoising_start is None:
             init_timestep = min(int(num_inference_steps * strength), num_inference_steps)
             t_start = max(num_inference_steps - init_timestep, 0)
-        else:
-            t_start = 0
 
-        timesteps = self.scheduler.timesteps[t_start * self.scheduler.order :]
+            timesteps = self.scheduler.timesteps[t_start * self.scheduler.order :]
+            if hasattr(self.scheduler, "set_begin_index"):
+                self.scheduler.set_begin_index(t_start * self.scheduler.order)
+
+            return timesteps, num_inference_steps - t_start
 
-        # Strength is irrelevant if we directly request a timestep to start at;
-        # that is, strength is determined by the denoising_start instead.
-        if denoising_start is not None:
+        else:
+            # Strength is irrelevant if we directly request a timestep to start at;
+            # that is, strength is determined by the denoising_start instead.
             discrete_timestep_cutoff = int(
                 round(
                     self.scheduler.config.num_train_timesteps
@@ -912,7 +914,7 @@ def get_timesteps(self, num_inference_steps, strength, device, denoising_start=N
                 )
             )
 
-            num_inference_steps = (timesteps < discrete_timestep_cutoff).sum().item()
+            num_inference_steps = (self.scheduler.timesteps < discrete_timestep_cutoff).sum().item()
             if self.scheduler.order == 2 and num_inference_steps % 2 == 0:
                 # if the scheduler is a 2nd order scheduler we might have to do +1
                 # because `num_inference_steps` might be even given that every timestep
@@ -923,11 +925,12 @@ def get_timesteps(self, num_inference_steps, strength, device, denoising_start=N
                 num_inference_steps = num_inference_steps + 1
 
             # because t_n+1 >= t_n, we slice the timesteps starting from the end
-            timesteps = timesteps[-num_inference_steps:]
+            t_start = len(self.scheduler.timesteps) - num_inference_steps
+            timesteps = self.scheduler.timesteps[t_start:]
+            if hasattr(self.scheduler, "set_begin_index"):
+                self.scheduler.set_begin_index(t_start)
             return timesteps, num_inference_steps
 
-        return timesteps, num_inference_steps - t_start
-
     # Copied from diffusers.pipelines.stable_diffusion_xl.pipeline_stable_diffusion_xl_img2img.StableDiffusionXLImg2ImgPipeline._get_add_time_ids
     def _get_add_time_ids(
         self,
diff --git a/src/diffusers/pipelines/stable_diffusion_xl/pipeline_stable_diffusion_xl_img2img.py b/src/diffusers/pipelines/stable_diffusion_xl/pipeline_stable_diffusion_xl_img2img.py
index ebabfe26aae4..29b5e11875fc 100644
--- a/src/diffusers/pipelines/stable_diffusion_xl/pipeline_stable_diffusion_xl_img2img.py
+++ b/src/diffusers/pipelines/stable_diffusion_xl/pipeline_stable_diffusion_xl_img2img.py
@@ -640,14 +640,16 @@ def get_timesteps(self, num_inference_steps, strength, device, denoising_start=N
         if denoising_start is None:
             init_timestep = min(int(num_inference_steps * strength), num_inference_steps)
             t_start = max(num_inference_steps - init_timestep, 0)
-        else:
-            t_start = 0
 
-        timesteps = self.scheduler.timesteps[t_start * self.scheduler.order :]
+            timesteps = self.scheduler.timesteps[t_start * self.scheduler.order :]
+            if hasattr(self.scheduler, "set_begin_index"):
+                self.scheduler.set_begin_index(t_start * self.scheduler.order)
+
+            return timesteps, num_inference_steps - t_start
 
-        # Strength is irrelevant if we directly request a timestep to start at;
-        # that is, strength is determined by the denoising_start instead.
-        if denoising_start is not None:
+        else:
+            # Strength is irrelevant if we directly request a timestep to start at;
+            # that is, strength is determined by the denoising_start instead.
             discrete_timestep_cutoff = int(
                 round(
                     self.scheduler.config.num_train_timesteps
@@ -655,7 +657,7 @@ def get_timesteps(self, num_inference_steps, strength, device, denoising_start=N
                 )
             )
 
-            num_inference_steps = (timesteps < discrete_timestep_cutoff).sum().item()
+            num_inference_steps = (self.scheduler.timesteps < discrete_timestep_cutoff).sum().item()
             if self.scheduler.order == 2 and num_inference_steps % 2 == 0:
                 # if the scheduler is a 2nd order scheduler we might have to do +1
                 # because `num_inference_steps` might be even given that every timestep
@@ -666,11 +668,12 @@ def get_timesteps(self, num_inference_steps, strength, device, denoising_start=N
                 num_inference_steps = num_inference_steps + 1
 
             # because t_n+1 >= t_n, we slice the timesteps starting from the end
-            timesteps = timesteps[-num_inference_steps:]
+            t_start = len(self.scheduler.timesteps) - num_inference_steps
+            timesteps = self.scheduler.timesteps[t_start:]
+            if hasattr(self.scheduler, "set_begin_index"):
+                self.scheduler.set_begin_index(t_start)
             return timesteps, num_inference_steps
 
-        return timesteps, num_inference_steps - t_start
-
     def prepare_latents(
         self, image, timestep, batch_size, num_images_per_prompt, dtype, device, generator=None, add_noise=True
     ):
diff --git a/src/diffusers/pipelines/stable_diffusion_xl/pipeline_stable_diffusion_xl_inpaint.py b/src/diffusers/pipelines/stable_diffusion_xl/pipeline_stable_diffusion_xl_inpaint.py
index 739f0c5c004b..d28a9afbfb7a 100644
--- a/src/diffusers/pipelines/stable_diffusion_xl/pipeline_stable_diffusion_xl_inpaint.py
+++ b/src/diffusers/pipelines/stable_diffusion_xl/pipeline_stable_diffusion_xl_inpaint.py
@@ -901,14 +901,16 @@ def get_timesteps(self, num_inference_steps, strength, device, denoising_start=N
         if denoising_start is None:
             init_timestep = min(int(num_inference_steps * strength), num_inference_steps)
             t_start = max(num_inference_steps - init_timestep, 0)
-        else:
-            t_start = 0
 
-        timesteps = self.scheduler.timesteps[t_start * self.scheduler.order :]
+            timesteps = self.scheduler.timesteps[t_start * self.scheduler.order :]
+            if hasattr(self.scheduler, "set_begin_index"):
+                self.scheduler.set_begin_index(t_start * self.scheduler.order)
+
+            return timesteps, num_inference_steps - t_start
 
-        # Strength is irrelevant if we directly request a timestep to start at;
-        # that is, strength is determined by the denoising_start instead.
-        if denoising_start is not None:
+        else:
+            # Strength is irrelevant if we directly request a timestep to start at;
+            # that is, strength is determined by the denoising_start instead.
             discrete_timestep_cutoff = int(
                 round(
                     self.scheduler.config.num_train_timesteps
@@ -916,7 +918,7 @@ def get_timesteps(self, num_inference_steps, strength, device, denoising_start=N
                 )
             )
 
-            num_inference_steps = (timesteps < discrete_timestep_cutoff).sum().item()
+            num_inference_steps = (self.scheduler.timesteps < discrete_timestep_cutoff).sum().item()
             if self.scheduler.order == 2 and num_inference_steps % 2 == 0:
                 # if the scheduler is a 2nd order scheduler we might have to do +1
                 # because `num_inference_steps` might be even given that every timestep
@@ -927,11 +929,12 @@ def get_timesteps(self, num_inference_steps, strength, device, denoising_start=N
                 num_inference_steps = num_inference_steps + 1
 
             # because t_n+1 >= t_n, we slice the timesteps starting from the end
-            timesteps = timesteps[-num_inference_steps:]
+            t_start = len(self.scheduler.timesteps) - num_inference_steps
+            timesteps = self.scheduler.timesteps[t_start:]
+            if hasattr(self.scheduler, "set_begin_index"):
+                self.scheduler.set_begin_index(t_start)
             return timesteps, num_inference_steps
 
-        return timesteps, num_inference_steps - t_start
-
     # Copied from diffusers.pipelines.stable_diffusion_xl.pipeline_stable_diffusion_xl_img2img.StableDiffusionXLImg2ImgPipeline._get_add_time_ids
     def _get_add_time_ids(
         self,