减少了一路视频vae解码

scripts/evaluation/world_model_interaction.py

@@ -444,7 +444,8 @@ def image_guided_synthesis_sim_mode(
         timestep_spacing: str = 'uniform',
         guidance_rescale: float = 0.0,
         sim_mode: bool = True,
-        **kwargs) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
+        decode_video: bool = True,
+        **kwargs) -> tuple[torch.Tensor | None, torch.Tensor, torch.Tensor]:
     """
     Performs image-guided video generation in a simulation-style mode with optional multimodal guidance (image, state, action, text).
 
@@ -467,10 +468,13 @@ def image_guided_synthesis_sim_mode(
         timestep_spacing (str): Timestep sampling method in DDIM sampler. Typically "uniform" or "linspace".
         guidance_rescale (float): Guidance rescaling factor to mitigate overexposure from classifier-free guidance.
         sim_mode (bool): Whether to perform world-model interaction or decision-making using the world-model.
+        decode_video (bool): Whether to decode latent samples to pixel-space video.
+            Set to False to skip VAE decode for speed when only actions/states are needed.
         **kwargs: Additional arguments passed to the DDIM sampler.
 
     Returns:
-        batch_variants (torch.Tensor): Predicted pixel-space video frames [B, C, T, H, W].
+        batch_variants (torch.Tensor | None): Predicted pixel-space video frames [B, C, T, H, W],
+            or None when decode_video=False.
         actions (torch.Tensor): Predicted action sequences [B, T, D] from diffusion decoding.
         states (torch.Tensor): Predicted state sequences [B, T, D] from diffusion decoding.
     """
@@ -554,6 +558,7 @@ def image_guided_synthesis_sim_mode(
     else:
         autocast_ctx = nullcontext()
 
+    batch_variants = None
     if ddim_sampler is not None:
         with autocast_ctx:
             samples, actions, states, intermedia = ddim_sampler.sample(
@@ -573,9 +578,10 @@ def image_guided_synthesis_sim_mode(
             guidance_rescale=guidance_rescale,
             **kwargs)
 
-        # Reconstruct from latent to pixel space
-        batch_images = model.decode_first_stage(samples)
-        batch_variants = batch_images
+        if decode_video:
+            # Reconstruct from latent to pixel space
+            batch_images = model.decode_first_stage(samples)
+            batch_variants = batch_images
 
     return batch_variants, actions, states
 
@@ -750,7 +756,8 @@ def run_inference(args: argparse.Namespace, gpu_num: int, gpu_no: int) -> None:
                     fs=model_input_fs,
                     timestep_spacing=args.timestep_spacing,
                     guidance_rescale=args.guidance_rescale,
-                    sim_mode=False)
+                    sim_mode=False,
+                    decode_video=not args.fast_policy_no_decode)
 
                 # Update future actions in the observation queues
                 for idx in range(len(pred_actions[0])):
@@ -808,11 +815,12 @@ def run_inference(args: argparse.Namespace, gpu_num: int, gpu_no: int) -> None:
                                                       observation)
 
                 # Save the imagen videos for decision-making
-                sample_tag = f"{args.dataset}-vid{sample['videoid']}-dm-fs-{fs}/itr-{itr}"
-                log_to_tensorboard(writer,
-                                   pred_videos_0,
-                                   sample_tag,
-                                   fps=args.save_fps)
+                if pred_videos_0 is not None:
+                    sample_tag = f"{args.dataset}-vid{sample['videoid']}-dm-fs-{fs}/itr-{itr}"
+                    log_to_tensorboard(writer,
+                                       pred_videos_0,
+                                       sample_tag,
+                                       fps=args.save_fps)
                 # Save videos environment changes via world-model interaction
                 sample_tag = f"{args.dataset}-vid{sample['videoid']}-wd-fs-{fs}/itr-{itr}"
                 log_to_tensorboard(writer,
@@ -821,10 +829,11 @@ def run_inference(args: argparse.Namespace, gpu_num: int, gpu_no: int) -> None:
                                    fps=args.save_fps)
 
                 # Save the imagen videos for decision-making
-                sample_video_file = f'{video_save_dir}/dm/{fs}/itr-{itr}.mp4'
-                save_results(pred_videos_0.cpu(),
-                             sample_video_file,
-                             fps=args.save_fps)
+                if pred_videos_0 is not None:
+                    sample_video_file = f'{video_save_dir}/dm/{fs}/itr-{itr}.mp4'
+                    save_results(pred_videos_0.cpu(),
+                                 sample_video_file,
+                                 fps=args.save_fps)
                 # Save videos environment changes via world-model interaction
                 sample_video_file = f'{video_save_dir}/wm/{fs}/itr-{itr}.mp4'
                 save_results(pred_videos_1.cpu(),
@@ -957,6 +966,11 @@ def get_parser():
                         action='store_true',
                         default=False,
                         help="not using the predicted states as comparison")
+    parser.add_argument(
+        "--fast_policy_no_decode",
+        action='store_true',
+        default=False,
+        help="Speed mode: policy pass only predicts actions, skip policy video decode/log/save.")
     parser.add_argument("--save_fps",
                         type=int,
                         default=8,