cleanup

liveportrait/live_portrait_pipeline.py

@@ -55,7 +55,6 @@ class LivePortraitPipeline(object):
             total_frames = driving_images.shape[0]
 
 
-
         disable_progress_bar = True if relative_motion_mode == "single_frame" else False
 
         source_info = crop_info["source_info"]

liveportrait/utils/crop.py

@@ -43,22 +43,16 @@ def _transform_img_kornia(img, M, dsize, device, flags='bilinear', borderMode='z
 
     # Convert M from numpy.ndarray to PyTorch tensor
     M = torch.from_numpy(M).float().to(device)
-    if M.shape == (3, 3):
-        M = M[:2, :].unsqueeze(0)  # Adjust M to the expected shape Bx2x3
-    elif M.shape == (2, 3):
-        M = M.unsqueeze(0)  # Add batch dimension if not present
     
-    # Reshape M for Kornia (1, 2, 3) and upscale to 3D affine matrix if not already
+    if M.shape == (3, 3):
+        M = M[:2, :]
     if M.shape == (2, 3):
-        M = M.unsqueeze(0)  # Add batch dimension
+        M = M.unsqueeze(0)
 
     # Convert image to floating point tensor if not already
     if img.dtype != torch.float32:
         img = img.float()
-    img = img.to(device)
-
-    # Reshape img for Kornia (B, C, H, W)
-    img = img.permute(0, 3, 1, 2)
+    img = img.permute(0, 3, 1, 2).to(device) # Reshape img for Kornia (B, C, H, W)
    
     # Apply the affine transformation
     img_warped = KGT.warp_affine(img, M, _dsize, mode=flags, padding_mode=borderMode)