Attempt to cast bias to f16.

utils/pipes.py

@@ -9,7 +9,6 @@ def create_stable_diffusion_xl_pipeline(device):
         "stabilityai/stable-diffusion-xl-base-1.0", torch_dtype=torch.float16, variant="fp16", use_safetensors=True
     )
     pipe = pipe.to(device)
-    pipe.vae.disable_vae_slicing()  # Experimental
     pipe.vae.disable_tiling()
     tiling.enable_circular_tiling([pipe.vae, pipe.text_encoder, pipe.unet])
     return pipe
@@ -20,7 +19,6 @@ def create_stable_diffusion_xl_img2img_pipe(device):
         "stabilityai/stable-diffusion-xl-refiner-1.0", torch_dtype=torch.float16, variant="fp16", use_safetensors=True
     )
     pipe = pipe.to(device)
-    pipe.vae.disable_vae_slicing()  # Experimental
     pipe.vae.disable_tiling()
     tiling.enable_circular_tiling([pipe.vae, pipe.text_encoder_2, pipe.unet])
     return pipe

utils/tiling.py

@@ -29,6 +29,10 @@ def asymmetric_conv2d_conv_forward(self, input: Tensor, weight: Tensor, bias: Op
     self.paddingY = (0, 0, self._reversed_padding_repeated_twice[2], self._reversed_padding_repeated_twice[3])
     working = F.pad(input, self.paddingX, mode='circular')
     working = F.pad(working, self.paddingY, mode='circular')
+
+    if bias is not None:
+        bias = bias.to(input.dtype)
+
     return F.conv2d(working, weight, bias, self.stride, _pair(0), self.dilation, self.groups)