convert mirrored latents back to torch

api/onnx_web/diffusers/patches/scheduler.py

@@ -1,6 +1,7 @@
 from typing import Any, Literal
 
 import numpy as np
+import torch
 from diffusers.schedulers.scheduling_utils import SchedulerOutput
 from torch import FloatTensor, Tensor
 
@@ -19,17 +20,17 @@ class SchedulerPatch:
     ) -> SchedulerOutput:
         result = self.wrapped.step(model_output, timestep, sample)
 
-        white_point = 0
-        black_point = 8
+        white_point = result.prev_sample.shape[2] // 8
+        black_point = result.prev_sample.shape[2] // 4
         center_line = result.prev_sample.shape[2] // 2
         direction = "horizontal"
 
         mirrored_latents = mirror_latents(
-            result.prev_sample, white_point, black_point, center_line, direction
+            result.prev_sample.numpy(), white_point, black_point, center_line, direction
         )
 
         return SchedulerOutput(
-            prev_sample=mirrored_latents,
+            prev_sample=torch.from_numpy(mirrored_latents),
         )