diff --git a/nodes/predict_gaussians_from_depth.py b/nodes/predict_gaussians_from_depth.py
index 1454e27..4489ef8 100644
--- a/nodes/predict_gaussians_from_depth.py
+++ b/nodes/predict_gaussians_from_depth.py
@@ -609,39 +609,40 @@ class SharpPredictGaussiansFromMetricDepth(io.ComfyNode):
             # Stats: report would-be shifts at 10 random gaussians + aggregate.
             # Print once per face (we have up to 42 faces — full per-face dump
             # would be noisy).
-            sample_g = torch.Generator(device="cpu").manual_seed(int(b))
-            sample_idx = torch.randperm(N_total, generator=sample_g)[:10]
-            _p(f"--- snap stats face {b} (mode={snap_to_external_depth}, "
-               f"would-be shifts at 10 random gaussians) ---")
-            _p(f"{'idx':>9} | {'lyr':>3} {'h':>4} {'w':>4} | "
-               f"{'z_now':>8} {'z_tgt':>8} {'dz':>8} | "
-               f"{'x_now':>7} {'x_tgt':>7} {'dx':>7} | "
-               f"{'y_now':>7} {'y_tgt':>7} {'dy':>7}")
-            for idx_t in sample_idx.tolist():
-                lyr = idx_t // (H_grid * W_grid)
-                rem = idx_t % (H_grid * W_grid)
-                hh = rem // W_grid
-                ww = rem % W_grid
-                z_now = float(current_z[idx_t])
-                z_tgt = float(target_z_flat[idx_t])
-                x_now = float(current_xx_ndc[idx_t])
-                x_tgt = float(base_xx_flat[idx_t])
-                y_now = float(current_yy_ndc[idx_t])
-                y_tgt = float(base_yy_flat[idx_t])
-                _p(f"{idx_t:>9d} | {lyr:>3d} {hh:>4d} {ww:>4d} | "
-                   f"{z_now:>8.3f} {z_tgt:>8.3f} {z_tgt - z_now:>+8.3f} | "
-                   f"{x_now:>+7.4f} {x_tgt:>+7.4f} {x_tgt - x_now:>+7.4f} | "
-                   f"{y_now:>+7.4f} {y_tgt:>+7.4f} {y_tgt - y_now:>+7.4f}")
-            dz_all = (target_z_flat - current_z).abs()
-            dx_all = (base_xx_flat - current_xx_ndc).abs()
-            dy_all = (base_yy_flat - current_yy_ndc).abs()
-            _p(f"aggregate |shift| over all {N_total} gaussians: "
-               f"|dz| mean={float(dz_all.mean()):.4f} "
-               f"max={float(dz_all.max()):.4f} | "
-               f"|dx_ndc| mean={float(dx_all.mean()):.5f} "
-               f"max={float(dx_all.max()):.5f} | "
-               f"|dy_ndc| mean={float(dy_all.mean()):.5f} "
-               f"max={float(dy_all.max()):.5f}")
+            # NOTE: tagged out — too verbose for normal use; uncomment to debug alignment.
+            # sample_g = torch.Generator(device="cpu").manual_seed(int(b))
+            # sample_idx = torch.randperm(N_total, generator=sample_g)[:10]
+            # _p(f"--- snap stats face {b} (mode={snap_to_external_depth}, "
+            #    f"would-be shifts at 10 random gaussians) ---")
+            # _p(f"{'idx':>9} | {'lyr':>3} {'h':>4} {'w':>4} | "
+            #    f"{'z_now':>8} {'z_tgt':>8} {'dz':>8} | "
+            #    f"{'x_now':>7} {'x_tgt':>7} {'dx':>7} | "
+            #    f"{'y_now':>7} {'y_tgt':>7} {'dy':>7}")
+            # for idx_t in sample_idx.tolist():
+            #     lyr = idx_t // (H_grid * W_grid)
+            #     rem = idx_t % (H_grid * W_grid)
+            #     hh = rem // W_grid
+            #     ww = rem % W_grid
+            #     z_now = float(current_z[idx_t])
+            #     z_tgt = float(target_z_flat[idx_t])
+            #     x_now = float(current_xx_ndc[idx_t])
+            #     x_tgt = float(base_xx_flat[idx_t])
+            #     y_now = float(current_yy_ndc[idx_t])
+            #     y_tgt = float(base_yy_flat[idx_t])
+            #     _p(f"{idx_t:>9d} | {lyr:>3d} {hh:>4d} {ww:>4d} | "
+            #        f"{z_now:>8.3f} {z_tgt:>8.3f} {z_tgt - z_now:>+8.3f} | "
+            #        f"{x_now:>+7.4f} {x_tgt:>+7.4f} {x_tgt - x_now:>+7.4f} | "
+            #        f"{y_now:>+7.4f} {y_tgt:>+7.4f} {y_tgt - y_now:>+7.4f}")
+            # dz_all = (target_z_flat - current_z).abs()
+            # dx_all = (base_xx_flat - current_xx_ndc).abs()
+            # dy_all = (base_yy_flat - current_yy_ndc).abs()
+            # _p(f"aggregate |shift| over all {N_total} gaussians: "
+            #    f"|dz| mean={float(dz_all.mean()):.4f} "
+            #    f"max={float(dz_all.max()):.4f} | "
+            #    f"|dx_ndc| mean={float(dx_all.mean()):.5f} "
+            #    f"max={float(dx_all.max()):.5f} | "
+            #    f"|dy_ndc| mean={float(dy_all.mean()):.5f} "
+            #    f"max={float(dy_all.max()):.5f}")
 
             if snap_to_external_depth == "z_only":
                 # Proportional rescale: mean_vectors *= target_z/current_z.
diff --git a/workflows/pano.json b/workflows/pano.json
index 595e6fc..68bef55 100644
--- a/workflows/pano.json
+++ b/workflows/pano.json
@@ -1 +1 @@
-{"id":"sharp-basic-workflow","revision":0,"last_node_id":92,"last_link_id":235,"nodes":[{"id":9,"type":"LoadSharpModel","pos":[-777.9320220314913,-1113.7600179184876],"size":[270,82],"flags":{},"order":0,"mode":0,"inputs":[{"localized_name":"precision","name":"precision","shape":7,"type":"COMBO","widget":{"name":"precision"},"link":null}],"outputs":[{"localized_name":"model_config","name":"model_config","type":"SHARP_MODEL_CONFIG","links":[189]}],"properties":{"cnr_id":"comfyui-sharp","ver":"51939f0d3bdcd9f4965978445ad0fa31a6222ef0","Node name for S&R":"LoadSharpModel","aux_id":"PozzettiAndrea/ComfyUI-Sharp"},"widgets_values":["auto"]},{"id":7,"type":"LoadImage","pos":[-1163.7622907189734,-982.6675026789543],"size":[662.2282800153919,367.6329862374266],"flags":{},"order":1,"mode":0,"inputs":[{"localized_name":"image","name":"image","type":"COMBO","widget":{"name":"image"},"link":null},{"localized_name":"choose file to upload","name":"upload","type":"IMAGEUPLOAD","widget":{"name":"upload"},"link":null}],"outputs":[{"localized_name":"IMAGE","name":"IMAGE","type":"IMAGE","links":[142,152]},{"localized_name":"MASK","name":"MASK","type":"MASK","links":null}],"properties":{"cnr_id":"comfy-core","ver":"0.12.2","Node name for S&R":"LoadImage"},"widgets_values":["pano.png","image"]},{"id":61,"type":"GetImageSize","pos":[-146.48160984051972,-587.5622109684546],"size":[140,124],"flags":{},"order":4,"mode":0,"inputs":[{"localized_name":"image","name":"image","type":"IMAGE","link":152}],"outputs":[{"localized_name":"width","name":"width","type":"INT","links":[153]},{"localized_name":"height","name":"height","type":"INT","links":[154]},{"localized_name":"batch_size","name":"batch_size","type":"INT","links":null}],"properties":{"cnr_id":"comfy-core","ver":"0.22.0","Node name for S&R":"GetImageSize"},"widgets_values":[]},{"id":68,"type":"PreviewImage","pos":[-96.00281033336206,-1272.2921412699745],"size":[176.03296710343238,247.28988659449237],"flags":{},"order":6,"mode":0,"inputs":[{"localized_name":"images","name":"images","type":"IMAGE","link":170}],"outputs":[],"properties":{"cnr_id":"comfy-core","ver":"0.22.0","Node name for S&R":"PreviewImage"},"widgets_values":[]},{"id":69,"type":"PreviewImage","pos":[840.6824076018588,-1319.5882703603213],"size":[140,246],"flags":{},"order":18,"mode":0,"inputs":[{"localized_name":"images","name":"images","type":"IMAGE","link":171}],"outputs":[],"properties":{"cnr_id":"comfy-core","ver":"0.22.0","Node name for S&R":"PreviewImage"},"widgets_values":[]},{"id":74,"type":"PreviewImage","pos":[1179.3418650271376,-1206.6176825825448],"size":[140,246],"flags":{},"order":19,"mode":0,"inputs":[{"localized_name":"images","name":"images","type":"IMAGE","link":181}],"outputs":[],"properties":{"cnr_id":"comfy-core","ver":"0.22.0","Node name for S&R":"PreviewImage"},"widgets_values":[]},{"id":58,"type":"PanoramaSplit","pos":[-455.8317763082749,-907.9551011844505],"size":[270,298],"flags":{},"order":5,"mode":0,"inputs":[{"localized_name":"panorama","name":"panorama","type":"PANORAMA","link":143},{"localized_name":"resolution","name":"resolution","type":"INT","widget":{"name":"resolution"},"link":null},{"localized_name":"subdivision","name":"subdivision","type":"COMBO","widget":{"name":"subdivision"},"link":null},{"localized_name":"fov_degrees","name":"fov_degrees","shape":7,"type":"FLOAT","widget":{"name":"fov_degrees"},"link":null},{"localized_name":"use_gpu","name":"use_gpu","shape":7,"type":"BOOLEAN","widget":{"name":"use_gpu"},"link":null},{"localized_name":"create_masks","name":"create_masks","shape":7,"type":"BOOLEAN","widget":{"name":"create_masks"},"link":null},{"localized_name":"mask_method","name":"mask_method","shape":7,"type":"COMBO","widget":{"name":"mask_method"},"link":null}],"outputs":[{"localized_name":"face_images","name":"face_images","type":"IMAGE","links":[170,202]},{"localized_name":"extrinsics","name":"extrinsics","type":"EXTRINSICS","links":[175]},{"localized_name":"intrinsics","name":"intrinsics","type":"INTRINSICS","links":[176]},{"localized_name":"fov_x_deg","name":"fov_x_deg","type":"FLOAT","links":null},{"localized_name":"face_masks","name":"face_masks","type":"MASK","links":null},{"localized_name":"debug_image","name":"debug_image","type":"IMAGE","links":null},{"localized_name":"debug_masks","name":"debug_masks","type":"IMAGE","links":null}],"properties":{"aux_id":"PozzettiAndrea/ComfyUI-PanoPack","ver":"adeea5953c01a5815d6ba5e33640e52fd7921df4","Node 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