[Schedulers] Fix callback steps

examples/community/composable_stable_diffusion.py

@@ -562,7 +562,8 @@ def __call__(
                 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 @@ def __call__(
 
             # 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 @@ def __call__(
 
             # 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 @@ def __call__(
                     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 @@ def __call__(
             # 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 @@ def __call__(
                 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 @@ def __call__(
                 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 @@ def __call__(
 
             # 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 @@ def __call__(
                 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 @@ def __call__(
                 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 @@ def __call__(
                 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 @@ def __call__(
                 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 @@ def __call__(
 
             # 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 @@ def __call__(
 
             # 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 @@ def __call__(
                 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 @@ def __call__(
                 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 @@ def __call__(
                 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 @@ def hacked_UpBlock2D_forward(self, hidden_states, res_hidden_states_tuple, temb=
                 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 @@ def __call__(
                 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 @@ def hacked_UpBlock2D_forward(self, hidden_states, res_hidden_states_tuple, temb=
                 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]

examples/community/stable_diffusion_repaint.py

@@ -932,7 +932,8 @@ def __call__(
                 # call the callback, if provided
                 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)
 
                 t_last = t
 

examples/community/stable_diffusion_xl_reference.py

@@ -771,7 +771,8 @@ def hacked_UpBlock2D_forward(self, hidden_states, res_hidden_states_tuple, temb=
                 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/wildcard_stable_diffusion.py

@@ -389,7 +389,8 @@ def __call__(
 
             # 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/research_projects/rdm/pipeline_rdm.py

@@ -432,7 +432,8 @@ def __call__(
 
             # 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)
         if not output_type == "latent":
             image = self.vae.decode(latents / self.vae.config.scaling_factor, return_dict=False)[0]
         else:

src/diffusers/pipelines/alt_diffusion/pipeline_alt_diffusion.py

@@ -736,7 +736,8 @@ def __call__(
                 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]

src/diffusers/pipelines/alt_diffusion/pipeline_alt_diffusion_img2img.py

@@ -765,7 +765,8 @@ def __call__(
                 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]

src/diffusers/pipelines/audioldm/pipeline_audioldm.py

@@ -542,7 +542,8 @@ def __call__(
                 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
         mel_spectrogram = self.decode_latents(latents)

src/diffusers/pipelines/audioldm2/pipeline_audioldm2.py

@@ -945,7 +945,8 @@ def __call__(
                 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)
 
         self.maybe_free_model_hooks()
 

src/diffusers/pipelines/controlnet/pipeline_controlnet.py

@@ -1005,7 +1005,8 @@ def __call__(
                 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

src/diffusers/pipelines/controlnet/pipeline_controlnet_img2img.py

@@ -1087,7 +1087,8 @@ def __call__(
                 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

src/diffusers/pipelines/controlnet/pipeline_controlnet_inpaint.py

@@ -1351,7 +1351,8 @@ def __call__(
                 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

src/diffusers/pipelines/controlnet/pipeline_controlnet_inpaint_sd_xl.py

@@ -1507,7 +1507,8 @@ def denoising_value_valid(dnv):
                 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)
 
         # make sure the VAE is in float32 mode, as it overflows in float16
         if self.vae.dtype == torch.float16 and self.vae.config.force_upcast:

src/diffusers/pipelines/controlnet/pipeline_controlnet_sd_xl.py

@@ -1158,7 +1158,8 @@ def __call__(
                 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)
 
         # manually for max memory savings
         if self.vae.dtype == torch.float16 and self.vae.config.force_upcast:

src/diffusers/pipelines/controlnet/pipeline_controlnet_sd_xl_img2img.py

@@ -1344,7 +1344,8 @@ def __call__(
                 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

src/diffusers/pipelines/kandinsky/pipeline_kandinsky.py

@@ -382,7 +382,8 @@ def __call__(
             ).prev_sample
 
             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)
 
         # post-processing
         image = self.movq.decode(latents, force_not_quantize=True)["sample"]

src/diffusers/pipelines/kandinsky/pipeline_kandinsky_img2img.py

@@ -475,7 +475,8 @@ def __call__(
             ).prev_sample
 
             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)
 
         # 7. post-processing
         image = self.movq.decode(latents, force_not_quantize=True)["sample"]

src/diffusers/pipelines/kandinsky/pipeline_kandinsky_inpaint.py

@@ -610,7 +610,8 @@ def __call__(
             ).prev_sample
 
             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)
 
         # post-processing
         image = self.movq.decode(latents, force_not_quantize=True)["sample"]

src/diffusers/pipelines/kandinsky2_2/pipeline_kandinsky2_2.py

@@ -244,7 +244,8 @@ def __call__(
             )[0]
 
             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)
         # post-processing
         image = self.movq.decode(latents, force_not_quantize=True)["sample"]
 

src/diffusers/pipelines/kandinsky2_2/pipeline_kandinsky2_2_controlnet.py

@@ -295,7 +295,8 @@ def __call__(
             )[0]
 
             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)
         # post-processing
         image = self.movq.decode(latents, force_not_quantize=True)["sample"]
 

src/diffusers/pipelines/kandinsky2_2/pipeline_kandinsky2_2_controlnet_img2img.py

@@ -355,7 +355,8 @@ def __call__(
             )[0]
 
             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)
 
         # post-processing
         image = self.movq.decode(latents, force_not_quantize=True)["sample"]