refactor get_timesteps for SDXL img2img + add set_begin_index (huggingface#9375)

* refator + add begin_index

* add kolors img2img to doc

Author: yiyixuxu

docs/source/en/api/pipelines/kolors.md

@@ -105,3 +105,11 @@ image.save("kolors_ipa_sample.png")
 
 - all
 - __call__
+
+## KolorsImg2ImgPipeline
+
+[[autodoc]] KolorsImg2ImgPipeline
+
+- all
+- __call__
+

src/diffusers/pipelines/controlnet/pipeline_controlnet_inpaint_sd_xl.py

@@ -1024,22 +1024,24 @@ 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
                     - (denoising_start * self.scheduler.config.num_train_timesteps)
                 )
             )
 
-            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,

src/diffusers/pipelines/kolors/pipeline_kolors_img2img.py

@@ -564,22 +564,24 @@ 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
                     - (denoising_start * self.scheduler.config.num_train_timesteps)
                 )
             )
 
-            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

src/diffusers/pipelines/pag/pipeline_pag_sd_xl_img2img.py

@@ -648,22 +648,24 @@ 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
                     - (denoising_start * self.scheduler.config.num_train_timesteps)
                 )
             )
 
-            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

src/diffusers/pipelines/pag/pipeline_pag_sd_xl_inpaint.py

@@ -897,22 +897,24 @@ 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
                     - (denoising_start * self.scheduler.config.num_train_timesteps)
                 )
             )
 
-            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,

src/diffusers/pipelines/stable_diffusion_xl/pipeline_stable_diffusion_xl_img2img.py

@@ -640,22 +640,24 @@ 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
                     - (denoising_start * self.scheduler.config.num_train_timesteps)
                 )
             )
 
-            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
     ):

src/diffusers/pipelines/stable_diffusion_xl/pipeline_stable_diffusion_xl_inpaint.py

@@ -901,22 +901,24 @@ 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
                     - (denoising_start * self.scheduler.config.num_train_timesteps)
                 )
             )
 
-            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,