diff --git a/projector.py b/projector.py
index 36041a086..fe39afef3 100755
--- a/projector.py
+++ b/projector.py
@@ -25,6 +25,8 @@
 def project(
     G,
     target: torch.Tensor, # [C,H,W] and dynamic range [0,255], W & H must match G output resolution
+    label,
+    label_dim,
     *,
     num_steps                  = 1000,
     w_avg_samples              = 10000,
@@ -35,7 +37,7 @@ def project(
     noise_ramp_length          = 0.75,
     regularize_noise_weight    = 1e5,
     verbose                    = False,
-    device: torch.device
+    device: torch.device,
 ):
     assert target.shape == (G.img_channels, G.img_resolution, G.img_resolution)
 
@@ -47,8 +49,15 @@ def logprint(*args):
 
     # Compute w stats.
     logprint(f'Computing W midpoint and stddev using {w_avg_samples} samples...')
+
     z_samples = np.random.RandomState(123).randn(w_avg_samples, G.z_dim)
-    w_samples = G.mapping(torch.from_numpy(z_samples).to(device), None)  # [N, L, C]
+
+    if label is not None and label_dim is not None:
+        onehot = np.zeros((w_avg_samples, label_dim), dtype=np.float32)
+        onehot[:, label] = 1
+        label = torch.Tensor(onehot).to(device)
+
+    w_samples = G.mapping(torch.from_numpy(z_samples).to(device), label)  # [N, L, C]
     w_samples = w_samples[:, :1, :].cpu().numpy().astype(np.float32)       # [N, 1, C]
     w_avg = np.mean(w_samples, axis=0, keepdims=True)      # [1, 1, C]
     w_std = (np.sum((w_samples - w_avg) ** 2) / w_avg_samples) ** 0.5
@@ -134,6 +143,8 @@ def logprint(*args):
 
 @click.command()
 @click.option('--network', 'network_pkl', help='Network pickle filename', required=True)
+@click.option('--label', help='Class Label', type=int, default=None, required=False)
+@click.option('--label-dim', help='Label Dimension', type=int, default=None, required=False)
 @click.option('--target', 'target_fname', help='Target image file to project to', required=True, metavar='FILE')
 @click.option('--num-steps',              help='Number of optimization steps', type=int, default=1000, show_default=True)
 @click.option('--seed',                   help='Random seed', type=int, default=303, show_default=True)
@@ -141,6 +152,8 @@ def logprint(*args):
 @click.option('--outdir',                 help='Where to save the output images', required=True, metavar='DIR')
 def run_projection(
     network_pkl: str,
+    label: int,
+    label_dim: int,
     target_fname: str,
     outdir: str,
     save_video: bool,
@@ -177,6 +190,8 @@ def run_projection(
     projected_w_steps = project(
         G,
         target=torch.tensor(target_uint8.transpose([2, 0, 1]), device=device), # pylint: disable=not-callable
+        label=label,
+        label_dim=label_dim,
         num_steps=num_steps,
         device=device,
         verbose=True