upscale_2: cast image to model's dtype

1 files changed, 4 insertions, 4 deletions

diff --git a/modules/upscaler_utils.py b/modules/upscaler_utils.py
index e4c63f09..5db74877 100644
--- a/modules/upscaler_utils.py
+++ b/modules/upscaler_utils.py
@@ -94,6 +94,7 @@ def tiled_upscale_2(
     tile_size: int,
     tile_overlap: int,
     scale: int,
+    device: torch.device,
     desc="Tiled upscale",
 ):
     # Alternative implementation of `upscale_with_model` originally used by
@@ -101,9 +102,6 @@ def tiled_upscale_2(
     # weighting is done in PyTorch space, as opposed to `images.Grid` doing it in
     # Pillow space without weighting.
 
-    # Grab the device the model is on, and use it.
-    device = torch_utils.get_param(model).device
-
     b, c, h, w = img.size()
     tile_size = min(tile_size, h, w)
 
@@ -175,7 +173,8 @@ def upscale_2(
     """
     Convenience wrapper around `tiled_upscale_2` that handles PIL images.
     """
-    tensor = pil_image_to_torch_bgr(img).float().unsqueeze(0)  # add batch dimension
+    param = torch_utils.get_param(model)
+    tensor = pil_image_to_torch_bgr(img).to(dtype=param.dtype).unsqueeze(0)  # add batch dimension
 
     with torch.no_grad():
         output = tiled_upscale_2(
@@ -185,5 +184,6 @@ def upscale_2(
             tile_overlap=tile_overlap,
             scale=scale,
             desc=desc,
+            device=param.device,
         )
     return torch_bgr_to_pil_image(output)