diff --git a/nerface_code/nerf-pytorch/nerf/train_utils.py b/nerface_code/nerf-pytorch/nerf/train_utils.py
index 1b9d56a6..abc88547 100644
--- a/nerface_code/nerf-pytorch/nerf/train_utils.py
+++ b/nerface_code/nerf-pytorch/nerf/train_utils.py
@@ -78,7 +78,8 @@ def predict_and_render_radiance(
     pts = ro[..., None, :] + rd[..., None, :] * z_vals[..., :, None]
     # Uncomment to dump a ply file visualizing camera rays and sampling points
     #dump_rays(ro.detach().cpu().numpy(), pts.detach().cpu().numpy())
-    ray_batch[...,3:6] = ray_dirs_fake[0][...,3:6] # TODO remove this this is for ablation of ray dir
+    if ray_dirs_fake:
+        ray_batch[...,3:6] = ray_dirs_fake[0][...,3:6] # TODO remove this this is for ablation of ray dir
 
     radiance_field = run_network(
         model_coarse,
@@ -178,6 +179,7 @@ def run_one_iter_of_nerf(
     latent_code = None,
     ray_directions_ablation = None
 ):
+    is_rad = torch.is_tensor(ray_directions_ablation)
     viewdirs = None
     if options.nerf.use_viewdirs:
         # Provide ray directions as input
@@ -203,44 +205,66 @@ def run_one_iter_of_nerf(
         #"caling normal rays (not NDC)"
         ro = ray_origins.view((-1, 3))
         rd = ray_directions.view((-1, 3))
-        rd_ablations = ray_directions_ablation.view((-1, 3))
+        if is_rad:
+            rd_ablations = ray_directions_ablation.view((-1, 3))
     near = options.dataset.near * torch.ones_like(rd[..., :1])
     far = options.dataset.far * torch.ones_like(rd[..., :1])
     rays = torch.cat((ro, rd, near, far), dim=-1)
-    rays_ablation = torch.cat((ro, rd_ablations, near, far), dim=-1)
+    if is_rad:
+        rays_ablation = torch.cat((ro, rd_ablations, near, far), dim=-1)
     # if options.nerf.use_viewdirs: # TODO uncomment
     #     rays = torch.cat((rays, viewdirs), dim=-1)
     #
     viewdirs = None  # TODO remove this paragraph
     if options.nerf.use_viewdirs:
         # Provide ray directions as input
-        viewdirs = ray_directions_ablation
-        viewdirs = viewdirs / viewdirs.norm(p=2, dim=-1).unsqueeze(-1)
-        viewdirs = viewdirs.view((-1, 3))
+        if is_rad:
+            viewdirs = ray_directions_ablation
+            viewdirs = viewdirs / viewdirs.norm(p=2, dim=-1).unsqueeze(-1)
+            viewdirs = viewdirs.view((-1, 3))
 
 
-    batches_ablation = get_minibatches(rays_ablation, chunksize=getattr(options.nerf, mode).chunksize)
+    if is_rad:
+        batches_ablation = get_minibatches(rays_ablation, chunksize=getattr(options.nerf, mode).chunksize)
     batches = get_minibatches(rays, chunksize=getattr(options.nerf, mode).chunksize)
     assert(batches[0].shape == batches[0].shape)
     background_prior = get_minibatches(background_prior, chunksize=getattr(options.nerf, mode).chunksize) if\
         background_prior is not None else background_prior
     #print("predicting")
-    pred = [
-        predict_and_render_radiance(
-            batch,
-            model_coarse,
-            model_fine,
-            options,
-            mode,
-            encode_position_fn=encode_position_fn,
-            encode_direction_fn=encode_direction_fn,
-            expressions = expressions,
-            background_prior = background_prior[i] if background_prior is not None else background_prior,
-            latent_code = latent_code,
-            ray_dirs_fake = batches_ablation
-        )
-        for i,batch in enumerate(batches)
-    ]
+    if is_rad:
+        pred = [
+            predict_and_render_radiance(
+                batch,
+                model_coarse,
+                model_fine,
+                options,
+                mode,
+                encode_position_fn=encode_position_fn,
+                encode_direction_fn=encode_direction_fn,
+                expressions = expressions,
+                background_prior = background_prior[i] if background_prior is not None else background_prior,
+                latent_code = latent_code,
+                ray_dirs_fake = batches_ablation
+            )
+            for i,batch in enumerate(batches)
+        ]
+    else:
+        pred = [
+            predict_and_render_radiance(
+                batch,
+                model_coarse,
+                model_fine,
+                options,
+                mode,
+                encode_position_fn=encode_position_fn,
+                encode_direction_fn=encode_direction_fn,
+                expressions = expressions,
+                background_prior = background_prior[i] if background_prior is not None else background_prior,
+                latent_code = latent_code,
+                ray_dirs_fake = None
+            )
+            for i,batch in enumerate(batches)
+        ]
     #print("predicted")
 
     synthesized_images = list(zip(*pred))