[IF| add set_begin_index for all IF pipelines (#7577)

add set_begin_index for all if pipelines

src/diffusers/pipelines/deepfloyd_if/pipeline_if.py

@@ -691,6 +691,9 @@ class IFPipeline(DiffusionPipeline, LoraLoaderMixin):
             self.scheduler.set_timesteps(num_inference_steps, device=device)
             timesteps = self.scheduler.timesteps
 
+        if hasattr(self.scheduler, "set_begin_index"):
+            self.scheduler.set_begin_index(0)
+
         # 5. Prepare intermediate images
         intermediate_images = self.prepare_intermediate_images(
             batch_size * num_images_per_prompt,

src/diffusers/pipelines/deepfloyd_if/pipeline_if_img2img.py

@@ -633,12 +633,15 @@ class IFImg2ImgPipeline(DiffusionPipeline, LoraLoaderMixin):
 
         return image
 
+    # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion_img2img.StableDiffusionImg2ImgPipeline.get_timesteps
     def get_timesteps(self, num_inference_steps, strength):
         # get the original timestep using init_timestep
         init_timestep = min(int(num_inference_steps * strength), num_inference_steps)
 
         t_start = max(num_inference_steps - init_timestep, 0)
-        timesteps = self.scheduler.timesteps[t_start:]
+        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
 

src/diffusers/pipelines/deepfloyd_if/pipeline_if_img2img_superresolution.py

@@ -714,13 +714,15 @@ class IFImg2ImgSuperResolutionPipeline(DiffusionPipeline, LoraLoaderMixin):
 
         return image
 
-    # Copied from diffusers.pipelines.deepfloyd_if.pipeline_if_img2img.IFImg2ImgPipeline.get_timesteps
+    # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion_img2img.StableDiffusionImg2ImgPipeline.get_timesteps
     def get_timesteps(self, num_inference_steps, strength):
         # get the original timestep using init_timestep
         init_timestep = min(int(num_inference_steps * strength), num_inference_steps)
 
         t_start = max(num_inference_steps - init_timestep, 0)
-        timesteps = self.scheduler.timesteps[t_start:]
+        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
 

src/diffusers/pipelines/deepfloyd_if/pipeline_if_inpainting.py

@@ -723,13 +723,15 @@ class IFInpaintingPipeline(DiffusionPipeline, LoraLoaderMixin):
 
         return mask_image
 
-    # Copied from diffusers.pipelines.deepfloyd_if.pipeline_if_img2img.IFImg2ImgPipeline.get_timesteps
+    # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion_img2img.StableDiffusionImg2ImgPipeline.get_timesteps
     def get_timesteps(self, num_inference_steps, strength):
         # get the original timestep using init_timestep
         init_timestep = min(int(num_inference_steps * strength), num_inference_steps)
 
         t_start = max(num_inference_steps - init_timestep, 0)
-        timesteps = self.scheduler.timesteps[t_start:]
+        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
 

src/diffusers/pipelines/deepfloyd_if/pipeline_if_inpainting_superresolution.py

@@ -800,13 +800,15 @@ class IFInpaintingSuperResolutionPipeline(DiffusionPipeline, LoraLoaderMixin):
 
         return mask_image
 
-    # Copied from diffusers.pipelines.deepfloyd_if.pipeline_if_img2img.IFImg2ImgPipeline.get_timesteps
+    # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion_img2img.StableDiffusionImg2ImgPipeline.get_timesteps
     def get_timesteps(self, num_inference_steps, strength):
         # get the original timestep using init_timestep
         init_timestep = min(int(num_inference_steps * strength), num_inference_steps)
 
         t_start = max(num_inference_steps - init_timestep, 0)
-        timesteps = self.scheduler.timesteps[t_start:]
+        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
 

src/diffusers/pipelines/deepfloyd_if/pipeline_if_superresolution.py

@@ -775,6 +775,9 @@ class IFSuperResolutionPipeline(DiffusionPipeline, LoraLoaderMixin):
             self.scheduler.set_timesteps(num_inference_steps, device=device)
             timesteps = self.scheduler.timesteps
 
+        if hasattr(self.scheduler, "set_begin_index"):
+            self.scheduler.set_begin_index(0)
+
         # 5. Prepare intermediate images
         num_channels = self.unet.config.in_channels // 2
         intermediate_images = self.prepare_intermediate_images(

src/diffusers/pipelines/pipeline_utils.py

@@ -998,6 +998,7 @@ class DiffusionPipeline(ConfigMixin, PushToHubMixin):
 
         all_model_components = {k: v for k, v in self.components.items() if isinstance(v, torch.nn.Module)}
 
+        self._all_hooks = []
         hook = None
         for model_str in self.model_cpu_offload_seq.split("->"):
             model = all_model_components.pop(model_str, None)