diff --git a/comfy_extras/nodes_trellis2.py b/comfy_extras/nodes_trellis2.py
index 266f67043..f57b1b018 100644
--- a/comfy_extras/nodes_trellis2.py
+++ b/comfy_extras/nodes_trellis2.py
@@ -334,12 +334,16 @@ class Trellis2UpsampleStage(IO.ComfyNode):
         )
 
     @staticmethod
-    def _quantize_unique(hr_coords: torch.Tensor, lr_resolution: int, hr_resolution: int) -> torch.Tensor:
-        # Fold the two scalar divisions into one and chain the float math in-place
-        # to avoid 3 full M*3 fp32 transients per call.
-        scale = (hr_resolution // 16) / lr_resolution
+    def _quantize_unique(hr_coords: torch.Tensor, lr_resolution: int, hr_resolution: int, pixal3d_mode: bool = False) -> torch.Tensor:
+        # Trellis2 uses `floor((c+0.5) * grid_res / lr_res)
+        # Pixal3D uses `round((c+0.5) * (grid_res-1) / lr_res)`
+        # this is a half-cell spatial shift. Branch so each upstream is matched bit-for-bit.
+        grid_res = hr_resolution // 16
         spatial = hr_coords[:, 1:].float()
-        spatial.add_(0.5).mul_(scale)
+        if pixal3d_mode:
+            spatial.add_(0.5).mul_((grid_res - 1) / lr_resolution).round_()
+        else:
+            spatial.add_(0.5).mul_(grid_res / lr_resolution)
         quant = torch.cat([hr_coords[:, :1], spatial.int()], dim=1)
         return quant.unique(dim=0)
 
@@ -352,6 +356,8 @@ class Trellis2UpsampleStage(IO.ComfyNode):
         shape_vae = vae.first_stage_model
         lr_resolution = 512
         target_resolution = int(target_resolution)
+        proj_pack = _proj_pack_from_conditioning(positive)
+        pixal3d_mode = proj_pack is not None
 
         # Decode each sample's HR coords, then search for the largest hr_resolution
         # that fits under max_tokens across all samples.
@@ -380,7 +386,7 @@ class Trellis2UpsampleStage(IO.ComfyNode):
             quant_unique_list = []
             exceeds_limit = False
             for hr_coords_i in sample_hr_coords:
-                qu = cls._quantize_unique(hr_coords_i, lr_resolution, hr_resolution)
+                qu = cls._quantize_unique(hr_coords_i, lr_resolution, hr_resolution, pixal3d_mode)
                 quant_unique_list.append(qu)
                 if qu.shape[0] >= max_tokens:
                     exceeds_limit = True
@@ -390,7 +396,7 @@ class Trellis2UpsampleStage(IO.ComfyNode):
             if hr_resolution <= 1024:
                 for k in range(len(quant_unique_list), len(sample_hr_coords)):
                     quant_unique_list.append(
-                        cls._quantize_unique(sample_hr_coords[k], lr_resolution, hr_resolution)
+                        cls._quantize_unique(sample_hr_coords[k], lr_resolution, hr_resolution, pixal3d_mode)
                     )
                 break
             hr_resolution -= 128
@@ -412,7 +418,6 @@ class Trellis2UpsampleStage(IO.ComfyNode):
             "trellis2_coords": coords,
             "trellis2_coord_counts": counts,
         }
-        proj_pack = _proj_pack_from_conditioning(positive)
         if proj_pack is not None:
             extras["trellis2_proj_feats"] = compute_stage_proj_feats(
                 proj_pack, "shape_1024", coords=coords, coord_resolution=coord_resolution,
@@ -1188,9 +1193,11 @@ class CFGGuidanceInterval(IO.ComfyNode):
     is done via model_sampling.percent_to_sigma so the window is portable
     across schedules (flow / EDM / discrete) and shift settings.
 
-    Defaults are full-range (no bypass). For Trellis2's upstream behavior,
-    wire (start_percent=0.0, end_percent=0.667) on the SS / shape KSamplers;
-    texture defaults to cfg=1 so the node is moot there."""
+    Defaults are full-range (no bypass). Upstream Trellis2 / Pixal3D
+    pipeline.json sets guidance_interval=[0.6, 1.0] (upstream t-space) on the
+    SS and shape samplers — CFG active only in the first 40% of sampling.
+    Wire (start_percent=0.0, end_percent=0.4) on the SS / shape KSamplers to
+    match. Texture defaults to cfg=1 so the node is moot there."""
     @classmethod
     def define_schema(cls):
         return IO.Schema(