diff --git a/tiled_diffusion.py b/tiled_diffusion.py
index 5292eac..816e099 100644
--- a/tiled_diffusion.py
+++ b/tiled_diffusion.py
@@ -552,8 +552,17 @@ def __call__(self, model_function: BaseModel.apply_model, args: dict):
             sigmas = self.sigmas = store.sigmas
             shift_method = store.model_options.get('tiled_diffusion_shift_method', 'random')
             seed = store.model_options.get('tiled_diffusion_seed', store.extra_args.get('seed', 0))
-            shift_height = torch.randint(0, self.tile_height, (len(sigmas)-1,), generator=torch.Generator(device='cpu').manual_seed(seed), device='cpu')
-            shift_width = torch.randint(0, self.tile_width, (len(sigmas)-1,), generator=torch.Generator(device='cpu').manual_seed(seed), device='cpu')
+            th = self.tile_height
+            tw = self.tile_width
+            cf = self.compression
+            if 'effnet' in c_in:
+                cf = x_in.shape[-1] * self.compression // c_in['effnet'].shape[-1] # compression factor
+                th = self.height // cf
+                tw = self.width // cf
+            shift_height = torch.randint(0, th, (len(sigmas)-1,), generator=torch.Generator(device='cpu').manual_seed(seed), device='cpu')
+            shift_height = (shift_height * cf / self.compression).round().to(torch.int32)
+            shift_width = torch.randint(0, tw, (len(sigmas)-1,), generator=torch.Generator(device='cpu').manual_seed(seed), device='cpu')
+            shift_width = (shift_width * cf / self.compression).round().to(torch.int32)
             if shift_method == "sorted":
                 shift_height = shift_height.sort().values
                 shift_width = shift_width.sort().values
@@ -585,6 +594,7 @@ def __call__(self, model_function: BaseModel.apply_model, args: dict):
                     if isinstance(v, torch.Tensor):
                         if len(v.shape) == len(x_tile.shape):
                             bboxes_ = bboxes
+                            sh_h_new, sh_w_new = sh_h, sh_w
                             if v.shape[-2:] != x_in.shape[-2:]:
                                 cf = x_in.shape[-1] * self.compression // v.shape[-1] # compression factor
                                 bboxes_ = self.get_grid_bbox(
@@ -597,6 +607,8 @@ def __call__(self, model_function: BaseModel.apply_model, args: dict):
                                     x_in.device,
                                     self.get_tile_weights,
                                 )
+                                sh_h_new, sh_w_new = round(sh_h * self.compression / cf), round(sh_w * self.compression / cf)
+                            v = v.roll(shifts=(sh_h_new, sh_w_new), dims=(-2,-1))
                             v = torch.cat([v[bbox_.slicer] for bbox_ in bboxes_[batch_id]])
                         if v.shape[0] != x_tile.shape[0]:
                             v = repeat_to_batch_size(v, x_tile.shape[0])