attempt to freeze scheduler timestep for panorama

api/onnx_web/diffusers/pipelines/panorama.py

@@ -612,6 +612,11 @@ class OnnxStableDiffusionPanoramaPipeline(DiffusionPipeline):
                         noise_pred_text - noise_pred_uncond
                     )
 
+                # freeze the scheduler's internal timestep
+                prev_step_index = None
+                if hasattr(self.scheduler, "_step_index"):
+                    prev_step_index = self.scheduler._step_index
+
                 # compute the previous noisy sample x_t -> x_t-1
                 scheduler_output = self.scheduler.step(
                     torch.from_numpy(noise_pred),
@@ -621,6 +626,10 @@ class OnnxStableDiffusionPanoramaPipeline(DiffusionPipeline):
                 )
                 latents_view_denoised = scheduler_output.prev_sample.numpy()
 
+                # reset the scheduler's internal timestep
+                if prev_step_index is not None:
+                    self.scheduler._step_index = prev_step_index
+
                 value[:, :, h_start:h_end, w_start:w_end] += latents_view_denoised
                 count[:, :, h_start:h_end, w_start:w_end] += 1
 
@@ -686,6 +695,11 @@ class OnnxStableDiffusionPanoramaPipeline(DiffusionPipeline):
                             * (region_noise_pred_text - region_noise_pred_uncond)
                         )
 
+                    # freeze the scheduler's internal timestep
+                    prev_step_index = None
+                    if hasattr(self.scheduler, "_step_index"):
+                        prev_step_index = self.scheduler._step_index
+
                     # compute the previous noisy sample x_t -> x_t-1
                     scheduler_output = self.scheduler.step(
                         torch.from_numpy(region_noise_pred),
@@ -695,6 +709,10 @@ class OnnxStableDiffusionPanoramaPipeline(DiffusionPipeline):
                     )
                     latents_region_denoised = scheduler_output.prev_sample.numpy()
 
+                    # reset the scheduler's internal timestep
+                    if prev_step_index is not None:
+                        self.scheduler._step_index = prev_step_index
+
                     if feather[0] > 0.0:
                         mask = make_tile_mask(
                             (h_end - h_start, w_end - w_start),
@@ -1027,6 +1045,11 @@ class OnnxStableDiffusionPanoramaPipeline(DiffusionPipeline):
                         noise_pred_text - noise_pred_uncond
                     )
 
+                # freeze the scheduler's internal timestep
+                prev_step_index = None
+                if hasattr(self.scheduler, "_step_index"):
+                    prev_step_index = self.scheduler._step_index
+
                 # compute the previous noisy sample x_t -> x_t-1
                 scheduler_output = self.scheduler.step(
                     torch.from_numpy(noise_pred),
@@ -1036,6 +1059,10 @@ class OnnxStableDiffusionPanoramaPipeline(DiffusionPipeline):
                 )
                 latents_view_denoised = scheduler_output.prev_sample.numpy()
 
+                # reset the scheduler's internal timestep
+                if prev_step_index is not None:
+                    self.scheduler._step_index = prev_step_index
+
                 value[:, :, h_start:h_end, w_start:w_end] += latents_view_denoised
                 count[:, :, h_start:h_end, w_start:w_end] += 1
 

api/onnx_web/diffusers/pipelines/panorama_xl.py

@@ -454,6 +454,11 @@ class StableDiffusionXLPanoramaPipelineMixin(StableDiffusionXLImg2ImgPipelineMix
                             guidance_rescale=guidance_rescale,
                         )
 
+                # freeze the scheduler's internal timestep
+                prev_step_index = None
+                if hasattr(self.scheduler, "_step_index"):
+                    prev_step_index = self.scheduler._step_index
+
                 # compute the previous noisy sample x_t -> x_t-1
                 scheduler_output = self.scheduler.step(
                     torch.from_numpy(noise_pred),
@@ -463,6 +468,10 @@ class StableDiffusionXLPanoramaPipelineMixin(StableDiffusionXLImg2ImgPipelineMix
                 )
                 latents_view_denoised = scheduler_output.prev_sample.numpy()
 
+                # reset the scheduler's internal timestep
+                if prev_step_index is not None:
+                    self.scheduler._step_index = prev_step_index
+
                 value[:, :, h_start:h_end, w_start:w_end] += latents_view_denoised
                 count[:, :, h_start:h_end, w_start:w_end] += 1
 
@@ -537,6 +546,11 @@ class StableDiffusionXLPanoramaPipelineMixin(StableDiffusionXLImg2ImgPipelineMix
                                 guidance_rescale=guidance_rescale,
                             )
 
+                    # freeze the scheduler's internal timestep
+                    prev_step_index = None
+                    if hasattr(self.scheduler, "_step_index"):
+                        prev_step_index = self.scheduler._step_index
+
                     # compute the previous noisy sample x_t -> x_t-1
                     scheduler_output = self.scheduler.step(
                         torch.from_numpy(region_noise_pred),
@@ -546,6 +560,10 @@ class StableDiffusionXLPanoramaPipelineMixin(StableDiffusionXLImg2ImgPipelineMix
                     )
                     latents_region_denoised = scheduler_output.prev_sample.numpy()
 
+                    # reset the scheduler's internal timestep
+                    if prev_step_index is not None:
+                        self.scheduler._step_index = prev_step_index
+
                     if feather[0] > 0.0:
                         mask = make_tile_mask(
                             (h_end - h_start, w_end - w_start),
@@ -878,6 +896,11 @@ class StableDiffusionXLPanoramaPipelineMixin(StableDiffusionXLImg2ImgPipelineMix
                             guidance_rescale=guidance_rescale,
                         )
 
+                # freeze the scheduler's internal timestep
+                prev_step_index = None
+                if hasattr(self.scheduler, "_step_index"):
+                    prev_step_index = self.scheduler._step_index
+
                 # compute the previous noisy sample x_t -> x_t-1
                 scheduler_output = self.scheduler.step(
                     torch.from_numpy(noise_pred),
@@ -887,6 +910,10 @@ class StableDiffusionXLPanoramaPipelineMixin(StableDiffusionXLImg2ImgPipelineMix
                 )
                 latents_view_denoised = scheduler_output.prev_sample.numpy()
 
+                # reset the scheduler's internal timestep
+                if prev_step_index is not None:
+                    self.scheduler._step_index = prev_step_index
+
                 value[:, :, h_start:h_end, w_start:w_end] += latents_view_denoised
                 count[:, :, h_start:h_end, w_start:w_end] += 1