feat: DINOv2 vitl14 fallback when DINOv3 gated access is pending

generate.py

@@ -81,6 +81,32 @@ def main():
     pipeline = Trellis2ImageTo3DPipeline.from_pretrained("microsoft/TRELLIS.2-4B")
     print(f"Loaded in {time.time() - t0:.0f}s")
 
+    # --- DINOv2 fallback (use when DINOv3 gated access is pending) ---
+    # DINOv2 vitl14 @ 448px → 1025 tokens = same as DINOv3 vitl16 @ 512px
+    import os
+    if os.environ.get("USE_DINOV2_FALLBACK", "0") == "1":
+        print("INFO: Using DINOv2 vitl14 fallback (DINOv3 access pending)")
+        import sys as _sys
+        _sys.path.insert(0, str(__file__).replace("generate.py", "TRELLIS.2"))
+        from trellis2.modules.image_feature_extractor import DinoV2FeatureExtractor
+        _orig_call = DinoV2FeatureExtractor.__call__
+        def _dinov2_call_448(self, image, **kwargs):
+            from PIL import Image as _PILImage
+            import numpy as _np, torch as _torch
+            if isinstance(image, list):
+                image = [i.resize((448, 448), _PILImage.LANCZOS) for i in image]
+                image = [_np.array(i.convert("RGB")).astype(_np.float32) / 255 for i in image]
+                image = [_torch.from_numpy(i).permute(2, 0, 1).float() for i in image]
+                image = _torch.stack(image).to(self.device)
+            import torch.nn.functional as _F
+            image = self.transform(image).to(self.device)
+            features = self.model(image, is_training=True)["x_prenorm"]
+            return _F.layer_norm(features, features.shape[-1:])
+        DinoV2FeatureExtractor.__call__ = _dinov2_call_448
+        pipeline.image_cond_model = DinoV2FeatureExtractor("dinov2_vitl14")
+        print("INFO: DINOv2 vitl14 loaded (448px → 1025 tokens)")
+    # --- end fallback ---
+
     # Move to MPS
     pipeline.to(torch.device("mps"))
     print("Device: MPS")