diff --git a/modules/processing_helpers.py b/modules/processing_helpers.py
index a334e2da6..f569a7d35 100644
--- a/modules/processing_helpers.py
+++ b/modules/processing_helpers.py
@@ -355,7 +355,7 @@ def resize_init_images(p):
     return p.width, p.height
 
 
-def resize_hires(p, latents): # input=latents output=pil
+def resize_hires(p, latents): # input=latents output=pil if not latent_upscaler else latent
     if not torch.is_tensor(latents):
         shared.log.warning('Hires: input is not tensor')
         first_pass_images = processing_vae.vae_decode(latents=latents, model=shared.sd_model, full_quality=p.full_quality, output_type='pil')
@@ -363,7 +363,7 @@ def resize_hires(p, latents): # input=latents output=pil
     latent_upscaler = shared.latent_upscale_modes.get(p.hr_upscaler, None)
     # shared.log.info(f'Hires: upscaler={p.hr_upscaler} width={p.hr_upscale_to_x} height={p.hr_upscale_to_y} images={latents.shape[0]}')
     if latent_upscaler is not None:
-        latents = torch.nn.functional.interpolate(latents, size=(p.hr_upscale_to_y // 8, p.hr_upscale_to_x // 8), mode=latent_upscaler["mode"], antialias=latent_upscaler["antialias"])
+        return torch.nn.functional.interpolate(latents, size=(p.hr_upscale_to_y // 8, p.hr_upscale_to_x // 8), mode=latent_upscaler["mode"], antialias=latent_upscaler["antialias"])
     first_pass_images = processing_vae.vae_decode(latents=latents, model=shared.sd_model, full_quality=p.full_quality, output_type='pil')
     resized_images = []
     for img in first_pass_images: