From f0a008279e856e36cd79c1c019248f52528144b6 Mon Sep 17 00:00:00 2001
From: s572915912 <54531516+s572915912@users.noreply.github.com>
Date: Fri, 11 Jul 2025 16:51:33 +0800
Subject: [PATCH] Update pipelines.py

---
 hy3dshape/hy3dshape/pipelines.py | 108 -------------------------------
 1 file changed, 108 deletions(-)

diff --git a/hy3dshape/hy3dshape/pipelines.py b/hy3dshape/hy3dshape/pipelines.py
index 0bb7c8f..71de472 100644
--- a/hy3dshape/hy3dshape/pipelines.py
+++ b/hy3dshape/hy3dshape/pipelines.py
@@ -781,111 +781,3 @@ class Hunyuan3DDiTFlowMatchingPipeline(Hunyuan3DDiTPipeline):
             box_v, mc_level, num_chunks, octree_resolution, mc_algo,
             enable_pbar=enable_pbar,
         )
-
-    @classmethod
-    @synchronize_timer('Hunyuan3DDiTFlowMatchingPipeline from Lightning Checkpoint')
-    def from_lightning_checkpoint(
-        cls,
-        ckpt_path: str,
-        config_path: str,
-        device: str = 'cuda',
-        dtype: torch.dtype = torch.float16,
-        **kwargs,
-    ):
-        """
-        Loads a model from a checkpoint created by the project's PyTorch Lightning training script.
-
-        This method correctly handles the nested configuration structure and state_dict prefixes
-        produced during training, and can intelligently load sharded checkpoints saved by Deepspeed.
-
-        Args:
-            ckpt_path (str): Path to the .ckpt checkpoint file or directory.
-            config_path (str): Path to the .yaml configuration file used for training.
-            device (str, optional): The device to load the model on. Defaults to 'cuda'.
-            dtype (torch.dtype, optional): The data type for the model. Defaults to torch.float16.
-
-        Returns:
-            Hunyuan3DDiTFlowMatchingPipeline: An instantiated pipeline ready for inference.
-        """
-        from omegaconf import OmegaConf
-        from hy3dshape.utils.misc import instantiate_from_config
-        from hy3dshape.schedulers import FlowMatchEulerDiscreteScheduler
-
-        logger.info(f"Loading model from Lightning checkpoint: {ckpt_path}")
-        logger.info(f"Using training config: {config_path}")
-
-        config = OmegaConf.load(config_path)
-
-        if os.path.isdir(ckpt_path):
-            # Assumes a Deepspeed-saved checkpoint directory
-            model_state_file = os.path.join(ckpt_path, 'checkpoint', 'mp_rank_00_model_states.pt')
-            if not os.path.exists(model_state_file):
-                raise FileNotFoundError(
-                    f"Could not find model weights file 'mp_rank_00_model_states.pt' in Deepspeed checkpoint directory: {os.path.join(ckpt_path, 'checkpoint')}"
-                )
-            
-            logger.info(f"Detected Deepspeed checkpoint directory, loading weights from: '{model_state_file}'")
-            ckpt = torch.load(model_state_file, map_location='cpu', weights_only=False)
-            # Deepspeed weights are often nested under the 'module' key
-            state_dict = ckpt.get('module', ckpt)
-        else:
-            # Standard .ckpt file
-            logger.info("Detected standard .ckpt file.")
-            ckpt = torch.load(ckpt_path, map_location='cpu', weights_only=False)
-            state_dict = ckpt.get('state_dict', ckpt)
-
-        # 1. Instantiate components that were frozen during training.
-        #    They will load their own pretrained weights upon instantiation.
-        logger.info("Instantiating VAE, Conditioner, and ImageProcessor...")
-        vae = instantiate_from_config(config.model.params.first_stage_config)
-        conditioner = instantiate_from_config(config.model.params.cond_stage_config)
-        image_processor = instantiate_from_config(config.model.params.image_processor_cfg)
-
-        # 2. Instantiate the component that was trained (the Denoiser).
-        logger.info("Instantiating Denoiser...")
-        denoiser = instantiate_from_config(config.model.params.denoiser_cfg)
-        
-        # 3. Load weights only for the Denoiser from our training checkpoint.
-        possible_prefixes = ["model.model.", "_forward_module.model.", "model."]
-        denoiser_dict = {}
-        matched_prefix = None
-        for prefix in possible_prefixes:
-            sub_dict = {k.replace(prefix, ''): v for k, v in state_dict.items() if k.startswith(prefix)}
-            if sub_dict:
-                denoiser_dict = sub_dict
-                matched_prefix = prefix
-                break
-                
-        if denoiser_dict:
-            logger.info(f"Successfully matched Denoiser weight prefix: '{matched_prefix}'")
-            missing_keys, unexpected_keys = denoiser.load_state_dict(denoiser_dict, strict=False)
-            logger.info(" Successfully loaded weights for 'denoiser'.")
-            if missing_keys:
-                logger.warning(f"  - Missing keys: {missing_keys}")
-            if unexpected_keys:
-                logger.warning(f"  - Unexpected keys: {unexpected_keys}")
-        else:
-            logger.warning("Could not find weights for 'denoiser' in checkpoint. It will be randomly initialized.")
-
-        # 4. Instantiate a new, inference-compatible scheduler.
-        logger.info("Creating a new scheduler for inference...")
-        scheduler = FlowMatchEulerDiscreteScheduler()
-        
-        # 5. Assemble the final, healthy pipeline.
-        pipeline = cls(
-            model=denoiser,
-            vae=vae,
-            scheduler=scheduler,
-            conditioner=conditioner,
-            image_processor=image_processor,
-            **kwargs,
-        )
-        
-        # 6. Move all model components to the correct device and set to evaluation mode.
-        pipeline.to(torch.device(device), dtype=dtype)
-        pipeline.model.eval()
-        pipeline.vae.eval()
-        pipeline.conditioner.eval()
-        
-        logger.info("\n Pipeline successfully assembled from Lightning checkpoint!")
-        return pipeline