Fix bug in panorama pipeline when using dpmsolver scheduler (#3499)

fix panorama pipeline with dpmsolver scheduler

src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_panorama.py

@@ -612,6 +612,7 @@ class StableDiffusionPanoramaPipeline(DiffusionPipeline, TextualInversionLoaderM
 
         # 6. Define panorama grid and initialize views for synthesis.
         views = self.get_views(height, width)
+        blocks_model_outputs = [None] * len(views)
         count = torch.zeros_like(latents)
         value = torch.zeros_like(latents)
 
@@ -632,7 +633,7 @@ class StableDiffusionPanoramaPipeline(DiffusionPipeline, TextualInversionLoaderM
                 # denoised (latent) crops are then averaged to produce the final latent
                 # for the current timestep via MultiDiffusion. Please see Sec. 4.1 in the
                 # MultiDiffusion paper for more details: https://arxiv.org/abs/2302.08113
-                for h_start, h_end, w_start, w_end in views:
+                for j, (h_start, h_end, w_start, w_end) in enumerate(views):
                     # get the latents corresponding to the current view coordinates
                     latents_for_view = latents[:, :, h_start:h_end, w_start:w_end]
 
@@ -656,9 +657,21 @@ class StableDiffusionPanoramaPipeline(DiffusionPipeline, TextualInversionLoaderM
                         noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
 
                     # compute the previous noisy sample x_t -> x_t-1
-                    latents_view_denoised = self.scheduler.step(
-                        noise_pred, t, latents_for_view, **extra_step_kwargs
-                    ).prev_sample
+                    if hasattr(self.scheduler, "model_outputs"):
+                        # rematch model_outputs in each block
+                        if i >= 1:
+                            self.scheduler.model_outputs = blocks_model_outputs[j]
+                        latents_view_denoised = self.scheduler.step(
+                            noise_pred, t, latents_for_view, **extra_step_kwargs
+                        ).prev_sample
+                        # collect model_outputs
+                        blocks_model_outputs[j] = [
+                            output if output is not None else None for output in self.scheduler.model_outputs
+                        ]
+                    else:
+                        latents_view_denoised = self.scheduler.step(
+                            noise_pred, t, latents_for_view, **extra_step_kwargs
+                        ).prev_sample
                     value[:, :, h_start:h_end, w_start:w_end] += latents_view_denoised
                     count[:, :, h_start:h_end, w_start:w_end] += 1