add view iteration to panoramic inpaint

api/onnx_web/chain/upscale_outpaint.py

@@ -123,6 +123,9 @@ def upscale_outpaint(
         draw_mask.rectangle((left, top, left + tile, top + tile), fill="black")
         return result.images[0]
 
+    if params.pipeline == "panorama":
+        logger.debug("outpainting with one shot panorama, no tiling")
+        return outpaint(source, (source.width, source.height, max(source.width, source.height)))
     if overlap == 0:
         logger.debug("outpainting with 0 margin, using grid tiling")
         output = process_tile_grid(source, SizeChart.auto, 1, [outpaint])

api/onnx_web/diffusers/pipelines/panorama.py

@@ -1088,30 +1088,48 @@ class OnnxStableDiffusionPanoramaPipeline(DiffusionPipeline):
         )
         timestep_dtype = ORT_TO_NP_TYPE[timestep_dtype]
 
+        # panorama additions
+        views = self.get_views(height, width)
+        count = np.zeros_like(latents)
+        value = np.zeros_like(latents)
+
         for i, t in enumerate(self.progress_bar(self.scheduler.timesteps)):
-            # expand the latents if we are doing classifier free guidance
+            count.fill(0)
-            latent_model_input = np.concatenate([latents] * 2) if do_classifier_free_guidance else latents
+            value.fill(0)
-            # concat latents, mask, masked_image_latnets in the channel dimension
-            latent_model_input = self.scheduler.scale_model_input(torch.from_numpy(latent_model_input), t)
-            latent_model_input = latent_model_input.cpu().numpy()
-            latent_model_input = np.concatenate([latent_model_input, mask, masked_image_latents], axis=1)
 
-            # predict the noise residual
+            for h_start, h_end, w_start, w_end in views:
-            timestep = np.array([t], dtype=timestep_dtype)
+                # get the latents corresponding to the current view coordinates
-            noise_pred = self.unet(sample=latent_model_input, timestep=timestep, encoder_hidden_states=prompt_embeds)[
+                latents_for_view = latents[:, :, h_start:h_end, w_start:w_end]
-                0
-            ]
 
-            # perform guidance
+                # expand the latents if we are doing classifier free guidance
-            if do_classifier_free_guidance:
+                latent_model_input = np.concatenate([latents_for_view] * 2) if do_classifier_free_guidance else latents_for_view
-                noise_pred_uncond, noise_pred_text = np.split(noise_pred, 2)
+                # concat latents, mask, masked_image_latnets in the channel dimension
-                noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
+                latent_model_input = self.scheduler.scale_model_input(torch.from_numpy(latent_model_input), t)
+                latent_model_input = latent_model_input.cpu().numpy()
+                latent_model_input = np.concatenate([latent_model_input, mask, masked_image_latents], axis=1)
 
-            # compute the previous noisy sample x_t -> x_t-1
+                # predict the noise residual
-            scheduler_output = self.scheduler.step(
+                timestep = np.array([t], dtype=timestep_dtype)
-                torch.from_numpy(noise_pred), t, torch.from_numpy(latents), **extra_step_kwargs
+                noise_pred = self.unet(sample=latent_model_input, timestep=timestep, encoder_hidden_states=prompt_embeds)[
-            )
+                    0
-            latents = scheduler_output.prev_sample.numpy()
+                ]
+
+                # perform guidance
+                if do_classifier_free_guidance:
+                    noise_pred_uncond, noise_pred_text = np.split(noise_pred, 2)
+                    noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
+
+                # compute the previous noisy sample x_t -> x_t-1
+                scheduler_output = self.scheduler.step(
+                    torch.from_numpy(noise_pred), t, torch.from_numpy(latents), **extra_step_kwargs
+                )
+                latents_view_denoised = scheduler_output.prev_sample.numpy()
+
+                value[:, :, h_start:h_end, w_start:w_end] += latents_view_denoised
+                count[:, :, h_start:h_end, w_start:w_end] += 1
+
+            # take the MultiDiffusion step. Eq. 5 in MultiDiffusion paper: https://arxiv.org/abs/2302.08113
+            latents = np.where(count > 0, value / count, value)
 
             # call the callback, if provided
             if callback is not None and i % callback_steps == 0: