[Schedulers] Fix callback steps (#5261)

* fix all

* make fix copies

* make fix copies

examples/community/composable_stable_diffusion.py

@@ -562,7 +562,8 @@ class ComposableStableDiffusionPipeline(DiffusionPipeline):
                 if i == len(timesteps) - 1 or ((i + 1) > num_warmup_steps and (i + 1) % self.scheduler.order == 0):
                     progress_bar.update()
                     if callback is not None and i % callback_steps == 0:
-                        callback(i, t, latents)
+                        step_idx = i // getattr(self.scheduler, "order", 1)
+                        callback(step_idx, t, latents)
 
         # 8. Post-processing
         image = self.decode_latents(latents)

examples/community/img2img_inpainting.py

@@ -434,7 +434,8 @@ class ImageToImageInpaintingPipeline(DiffusionPipeline):
 
             # call the callback, if provided
             if callback is not None and i % callback_steps == 0:
-                callback(i, t, latents)
+                step_idx = i // getattr(self.scheduler, "order", 1)
+                callback(step_idx, t, latents)
 
         latents = 1 / 0.18215 * latents
         image = self.vae.decode(latents).sample

examples/community/interpolate_stable_diffusion.py

@@ -372,7 +372,8 @@ class StableDiffusionWalkPipeline(DiffusionPipeline):
 
             # call the callback, if provided
             if callback is not None and i % callback_steps == 0:
-                callback(i, t, latents)
+                step_idx = i // getattr(self.scheduler, "order", 1)
+                callback(step_idx, t, latents)
 
         latents = 1 / 0.18215 * latents
         image = self.vae.decode(latents).sample

examples/community/lpw_stable_diffusion.py

@@ -1088,7 +1088,8 @@ class StableDiffusionLongPromptWeightingPipeline(
                     progress_bar.update()
                     if i % callback_steps == 0:
                         if callback is not None:
-                            callback(i, t, latents)
+                            step_idx = i // getattr(self.scheduler, "order", 1)
+                            callback(step_idx, t, latents)
                         if is_cancelled_callback is not None and is_cancelled_callback():
                             return None
 

examples/community/lpw_stable_diffusion_onnx.py

@@ -846,7 +846,8 @@ class OnnxStableDiffusionLongPromptWeightingPipeline(OnnxStableDiffusionPipeline
             # call the callback, if provided
             if i % callback_steps == 0:
                 if callback is not None:
-                    callback(i, t, latents)
+                    step_idx = i // getattr(self.scheduler, "order", 1)
+                    callback(step_idx, t, latents)
                 if is_cancelled_callback is not None and is_cancelled_callback():
                     return None
 

examples/community/lpw_stable_diffusion_xl.py

@@ -1182,7 +1182,8 @@ class SDXLLongPromptWeightingPipeline(DiffusionPipeline, FromSingleFileMixin, Lo
                 if i == len(timesteps) - 1 or ((i + 1) > num_warmup_steps and (i + 1) % self.scheduler.order == 0):
                     progress_bar.update()
                     if callback is not None and i % callback_steps == 0:
-                        callback(i, t, latents)
+                        step_idx = i // getattr(self.scheduler, "order", 1)
+                        callback(step_idx, t, latents)
 
         if not output_type == "latent":
             # make sure the VAE is in float32 mode, as it overflows in float16

examples/community/masked_stable_diffusion_img2img.py

@@ -202,7 +202,8 @@ class MaskedStableDiffusionImg2ImgPipeline(StableDiffusionImg2ImgPipeline):
                 if i == len(timesteps) - 1 or ((i + 1) > num_warmup_steps and (i + 1) % self.scheduler.order == 0):
                     progress_bar.update()
                     if callback is not None and i % callback_steps == 0:
-                        callback(i, t, latents)
+                        step_idx = i // getattr(self.scheduler, "order", 1)
+                        callback(step_idx, t, latents)
 
         if not output_type == "latent":
             scaled = latents / self.vae.config.scaling_factor

examples/community/multilingual_stable_diffusion.py

@@ -407,7 +407,8 @@ class MultilingualStableDiffusion(DiffusionPipeline):
 
             # call the callback, if provided
             if callback is not None and i % callback_steps == 0:
-                callback(i, t, latents)
+                step_idx = i // getattr(self.scheduler, "order", 1)
+                callback(step_idx, t, latents)
 
         latents = 1 / 0.18215 * latents
         image = self.vae.decode(latents).sample

examples/community/pipeline_prompt2prompt.py

@@ -254,7 +254,8 @@ class Prompt2PromptPipeline(StableDiffusionPipeline):
                 if i == len(timesteps) - 1 or ((i + 1) > num_warmup_steps and (i + 1) % self.scheduler.order == 0):
                     progress_bar.update()
                     if callback is not None and i % callback_steps == 0:
-                        callback(i, t, latents)
+                        step_idx = i // getattr(self.scheduler, "order", 1)
+                        callback(step_idx, t, latents)
 
         # 8. Post-processing
         if not output_type == "latent":

examples/community/pipeline_zero1to3.py

@@ -865,7 +865,8 @@ class Zero1to3StableDiffusionPipeline(DiffusionPipeline):
                 if i == len(timesteps) - 1 or ((i + 1) > num_warmup_steps and (i + 1) % self.scheduler.order == 0):
                     progress_bar.update()
                     if callback is not None and i % callback_steps == 0:
-                        callback(i, t, latents)
+                        step_idx = i // getattr(self.scheduler, "order", 1)
+                        callback(step_idx, t, latents)
 
         # 8. Post-processing
         has_nsfw_concept = None

examples/community/run_onnx_controlnet.py

@@ -815,7 +815,8 @@ class OnnxStableDiffusionControlNetImg2ImgPipeline(DiffusionPipeline):
                 if i == len(timesteps) - 1 or ((i + 1) > num_warmup_steps and (i + 1) % self.scheduler.order == 0):
                     progress_bar.update()
                     if callback is not None and i % callback_steps == 0:
-                        callback(i, t, latents)
+                        step_idx = i // getattr(self.scheduler, "order", 1)
+                        callback(step_idx, t, latents)
 
         if not output_type == "latent":
             _latents = latents.cpu().detach().numpy() / 0.18215

examples/community/run_tensorrt_controlnet.py

@@ -919,7 +919,8 @@ class TensorRTStableDiffusionControlNetImg2ImgPipeline(DiffusionPipeline):
                 if i == len(timesteps) - 1 or ((i + 1) > num_warmup_steps and (i + 1) % self.scheduler.order == 0):
                     progress_bar.update()
                     if callback is not None and i % callback_steps == 0:
-                        callback(i, t, latents)
+                        step_idx = i // getattr(self.scheduler, "order", 1)
+                        callback(step_idx, t, latents)
 
         if not output_type == "latent":
             _latents = latents.cpu().detach().numpy() / 0.18215

examples/community/seed_resize_stable_diffusion.py

@@ -337,7 +337,8 @@ class SeedResizeStableDiffusionPipeline(DiffusionPipeline):
 
             # call the callback, if provided
             if callback is not None and i % callback_steps == 0:
-                callback(i, t, latents)
+                step_idx = i // getattr(self.scheduler, "order", 1)
+                callback(step_idx, t, latents)
 
         latents = 1 / 0.18215 * latents
         image = self.vae.decode(latents).sample

examples/community/speech_to_image_diffusion.py

@@ -242,7 +242,8 @@ class SpeechToImagePipeline(DiffusionPipeline):
 
             # call the callback, if provided
             if callback is not None and i % callback_steps == 0:
-                callback(i, t, latents)
+                step_idx = i // getattr(self.scheduler, "order", 1)
+                callback(step_idx, t, latents)
 
         latents = 1 / 0.18215 * latents
         image = self.vae.decode(latents).sample

examples/community/stable_diffusion_controlnet_img2img.py

@@ -951,7 +951,8 @@ class StableDiffusionControlNetImg2ImgPipeline(DiffusionPipeline):
                 if i == len(timesteps) - 1 or ((i + 1) > num_warmup_steps and (i + 1) % self.scheduler.order == 0):
                     progress_bar.update()
                     if callback is not None and i % callback_steps == 0:
-                        callback(i, t, latents)
+                        step_idx = i // getattr(self.scheduler, "order", 1)
+                        callback(step_idx, t, latents)
 
         # If we do sequential model offloading, let's offload unet and controlnet
         # manually for max memory savings

examples/community/stable_diffusion_controlnet_inpaint.py

@@ -1100,7 +1100,8 @@ class StableDiffusionControlNetInpaintPipeline(DiffusionPipeline):
                 if i == len(timesteps) - 1 or ((i + 1) > num_warmup_steps and (i + 1) % self.scheduler.order == 0):
                     progress_bar.update()
                     if callback is not None and i % callback_steps == 0:
-                        callback(i, t, latents)
+                        step_idx = i // getattr(self.scheduler, "order", 1)
+                        callback(step_idx, t, latents)
 
         # If we do sequential model offloading, let's offload unet and controlnet
         # manually for max memory savings

examples/community/stable_diffusion_controlnet_inpaint_img2img.py

@@ -1081,7 +1081,8 @@ class StableDiffusionControlNetInpaintImg2ImgPipeline(DiffusionPipeline):
                 if i == len(timesteps) - 1 or ((i + 1) > num_warmup_steps and (i + 1) % self.scheduler.order == 0):
                     progress_bar.update()
                     if callback is not None and i % callback_steps == 0:
-                        callback(i, t, latents)
+                        step_idx = i // getattr(self.scheduler, "order", 1)
+                        callback(step_idx, t, latents)
 
         # If we do sequential model offloading, let's offload unet and controlnet
         # manually for max memory savings

examples/community/stable_diffusion_controlnet_reference.py

@@ -802,7 +802,8 @@ class StableDiffusionControlNetReferencePipeline(StableDiffusionControlNetPipeli
                 if i == len(timesteps) - 1 or ((i + 1) > num_warmup_steps and (i + 1) % self.scheduler.order == 0):
                     progress_bar.update()
                     if callback is not None and i % callback_steps == 0:
-                        callback(i, t, latents)
+                        step_idx = i // getattr(self.scheduler, "order", 1)
+                        callback(step_idx, t, latents)
 
         # If we do sequential model offloading, let's offload unet and controlnet
         # manually for max memory savings

examples/community/stable_diffusion_ipex.py

@@ -817,7 +817,8 @@ class StableDiffusionIPEXPipeline(DiffusionPipeline):
                 if i == len(timesteps) - 1 or ((i + 1) > num_warmup_steps and (i + 1) % self.scheduler.order == 0):
                     progress_bar.update()
                     if callback is not None and i % callback_steps == 0:
-                        callback(i, t, latents)
+                        step_idx = i // getattr(self.scheduler, "order", 1)
+                        callback(step_idx, t, latents)
 
         if output_type == "latent":
             image = latents

examples/community/stable_diffusion_reference.py

@@ -770,7 +770,8 @@ class StableDiffusionReferencePipeline(StableDiffusionPipeline):
                 if i == len(timesteps) - 1 or ((i + 1) > num_warmup_steps and (i + 1) % self.scheduler.order == 0):
                     progress_bar.update()
                     if callback is not None and i % callback_steps == 0:
-                        callback(i, t, latents)
+                        step_idx = i // getattr(self.scheduler, "order", 1)
+                        callback(step_idx, t, latents)
 
         if not output_type == "latent":
             image = self.vae.decode(latents / self.vae.config.scaling_factor, return_dict=False)[0]