diff --git a/modules/rife/__init__.py b/modules/rife/__init__.py
index b8e9fbc6f..c4db35645 100644
--- a/modules/rife/__init__.py
+++ b/modules/rife/__init__.py
@@ -82,13 +82,13 @@ def interpolate(images: list, count: int = 2, scale: float = 1.0, pad: int = 1,
     for _i in range(pad): # fill starting frames
         buffer.put(frame)
 
-    I1 = f_pad(torch.from_numpy(np.transpose(frame, (2,0,1))).to(devices.device).unsqueeze(0).float() / 255.)
+    I1 = f_pad(torch.from_numpy(np.transpose(frame, (2,0,1))).to(devices.device).unsqueeze(0).float() / 255.0)
     with torch.no_grad():
         with tqdm(total=len(images), desc='Interpolate', unit='frame') as pbar:
             for image in images:
                 frame = cv2.cvtColor(np.array(image), cv2.COLOR_RGB2BGR)
                 I0 = I1
-                I1 = f_pad(torch.from_numpy(np.transpose(frame, (2,0,1))).to(devices.device).unsqueeze(0).float() / 255.)
+                I1 = f_pad(torch.from_numpy(np.transpose(frame, (2,0,1))).to(devices.device).unsqueeze(0).float() / 255.0)
                 I0_small = F.interpolate(I0, (32, 32), mode='bilinear', align_corners=False).to(torch.float32)
                 I1_small = F.interpolate(I1, (32, 32), mode='bilinear', align_corners=False).to(torch.float32)
                 ssim = ssim_matlab(I0_small[:, :3], I1_small[:, :3])
@@ -104,7 +104,7 @@ def interpolate(images: list, count: int = 2, scale: float = 1.0, pad: int = 1,
                 else:
                     output = execute(I0, I1, count-1)
                 for mid in output:
-                    mid = (((mid[0] * 255.).byte().cpu().numpy().transpose(1, 2, 0)))
+                    mid = (((mid[0] * 255.0).byte().cpu().numpy().transpose(1, 2, 0)))
                     buffer.put(mid[:h, :w])
                 buffer.put(frame)
                 pbar.update(1)
@@ -116,3 +116,37 @@ def interpolate(images: list, count: int = 2, scale: float = 1.0, pad: int = 1,
     t1 = time.time()
     shared.log.info(f'Video interpolate: input={len(images)} frames={len(interpolated)} buffer={buffer.qsize()} duplicate={duplicate} width={w} height={h} interpolate={count} scale={scale} pad={pad} change={change} time={round(t1 - t0, 2)}')
     return interpolated
+
+
+def interpolate_nchw(images: list, count: int = 2, scale: float = 1.0):
+    if images is None or len(images) < 2:
+        return images
+    if model is None:
+        load()
+    interpolated = []
+    _n, _c, h, w = images.shape
+    t0 = time.time()
+
+    def f_pad(img):
+        return F.pad(img, padding).to(device=devices.device, dtype=devices.dtype) # pylint: disable=not-callable
+
+    tmp = max(128, int(128 / scale))
+    ph = ((h - 1) // tmp + 1) * tmp
+    pw = ((w - 1) // tmp + 1) * tmp
+    padding = (0, pw - w, 0, ph - h)
+
+    I1 = f_pad(images[0].unsqueeze(0))
+    with torch.no_grad():
+        with tqdm(total=len(images), desc='Interpolate', unit='frame') as pbar:
+            for frame in images:
+                I0 = I1
+                I1 = f_pad(frame.unsqueeze(0))
+                for i in range(count-1):
+                    output = model.inference(I0, I1, (i+1) * 1. / (count), scale)
+                    interpolated.append(output)
+                interpolated.append(I1)
+                pbar.update(1)
+
+    t1 = time.time()
+    shared.log.info(f'Video interpolate: input={len(images)} frames={len(interpolated)} width={w} height={h} interpolate={count} scale={scale} time={round(t1 - t0, 2)}')
+    return interpolated