added bpt

hy3dgen/shapegen/postprocessors.py

@@ -158,6 +158,83 @@ def import_mesh(mesh: Union[pymeshlab.MeshSet, trimesh.Trimesh, Latent2MeshOutpu
 
     return mesh
 
+def bpt_remesh(self, mesh: trimesh.Trimesh, verbose: bool = False, max_seq_len:int=10000, cond_dim:int=768, pc_num: int=8192, with_normal: bool = True, kwarg_k: int = 50, kwarg_p: float = 0.95):
+        from .bpt.model import data_utils
+        from .bpt.model.model import MeshTransformer
+        from .bpt.model.serializaiton import BPT_deserialize
+        from .bpt.utils import sample_pc, joint_filter
+
+        pc_normal = sample_pc(mesh, pc_num=pc_num, with_normal=with_normal)
+
+        pc_normal = pc_normal[None, :, :] if len(pc_normal.shape) == 2 else pc_normal
+
+        from torch.serialization import add_safe_globals
+        from deepspeed.runtime.fp16.loss_scaler import LossScaler
+        from deepspeed.runtime.zero.config import ZeroStageEnum
+        from deepspeed.utils.tensor_fragment import fragment_address
+
+        add_safe_globals([LossScaler, fragment_address, ZeroStageEnum])
+
+        model = MeshTransformer(cond_dim=cond_dim, max_seq_len=max_seq_len)
+
+        comfyui_dir = os.path.dirname(os.path.abspath(__file__)) 
+        model_path = os.path.join(comfyui_dir, 'bpt/bpt-8-16-500m.pt')
+        print(model_path)
+        model.load(model_path)
+        model = model.eval()
+        model = model.half()
+        model = model.cuda()
+
+        import torch
+        pc_tensor = torch.from_numpy(pc_normal).cuda().half()
+        if len(pc_tensor.shape) == 2:
+            pc_tensor = pc_tensor.unsqueeze(0)
+
+        codes = model.generate(
+            pc=pc_tensor,
+            filter_logits_fn=joint_filter,
+            filter_kwargs=dict(k=50, p=0.95),
+            return_codes=True,
+        )
+
+        coords = []
+        try:
+            for i in range(len(codes)):
+                code = codes[i]
+                code = code[code != model.pad_id].cpu().numpy()
+                vertices = BPT_deserialize(
+                    code,
+                    block_size=model.block_size,
+                    offset_size=model.offset_size,
+                    use_special_block=model.use_special_block,
+                )
+                coords.append(vertices)
+        except:
+            coords.append(np.zeros(3, 3))
+
+        # convert coordinates to mesh
+        vertices = coords[0]
+        faces = torch.arange(1, len(vertices) + 1).view(-1, 3)
+
+        # Move to CPU
+        faces = faces.cpu().numpy()
+
+        del model
+
+        return data_utils.to_mesh(vertices, faces, transpose=False, post_process=True)
+
+class BptMesh:
+    def __call__(
+        self,
+        mesh: Union[pymeshlab.MeshSet, trimesh.Trimesh, Latent2MeshOutput, str],
+        max_seq_len: int = 10000,
+        cond_dim: int = 768,
+        kwarg_k: int = 50,
+        kwarg_p: float = 0.95,
+        verbose: bool = False 
+    ) -> Union[pymeshlab.MeshSet, trimesh.Trimesh]:
+        mesh = bpt_remesh(self, mesh=mesh, cond_dim=cond_dim, max_seq_len=max_seq_len, kwarg_k=kwarg_k, kwarg_p=kwarg_p)
+        return mesh
 
 class FaceReducer:
     def __call__(