保存结果一次

scripts/evaluation/world_model_interaction.py

@@ -239,6 +239,8 @@ class InteractionAnalysisLogger:
         'round_id',
         'step',
         'step_time_s',
+        'backbone_reused_blocks_count',
+        'backbone_reuse_hit_blocks',
         'latent_delta',
         'action_delta',
         'state_delta',
@@ -388,6 +390,10 @@ class InteractionAnalysisLogger:
                 'round_id': round_id,
                 'step': record['step_index'],
                 'step_time_s': float(record['step_time_s']),
+                'backbone_reused_blocks_count': int(
+                    record.get('backbone_reused_blocks_count', 0)),
+                'backbone_reuse_hit_blocks':
+                record.get('backbone_reuse_hit_blocks', ''),
                 'latent_delta': latent_delta,
                 'action_delta': action_delta,
                 'state_delta': state_delta,
@@ -928,7 +934,7 @@ def image_guided_synthesis_sim_mode(
         return_debug_info (bool): Whether to return per-step traces for analysis logging.
         **kwargs: Additional arguments passed to the DDIM sampler, including
             sparse head controls such as `head_schedule`, `head_log_steps`,
-            and `head_skip_mode`.
+            and `head_skip_mode`, plus optional decoder block reuse settings.
 
     Returns:
         batch_variants (torch.Tensor | None): Predicted pixel-space video frames [B, C, T, H, W].
@@ -1051,16 +1057,27 @@ def run_inference(args: argparse.Namespace, gpu_num: int, gpu_no: int) -> None:
     head_schedule = args.head_schedule_steps if args.head_schedule_steps else None
     head_log_steps = args.head_log_steps if args.head_log_steps else None
     head_skip_mode = args.head_skip_mode
+    backbone_reuse_blocks = (args.backbone_reuse_blocks
+                             if args.backbone_reuse_blocks else None)
+    backbone_reuse_schedule_steps = (
+        args.backbone_reuse_schedule_steps
+        if args.backbone_reuse_schedule_steps else None)
+    backbone_reuse_force_compute_steps = (
+        args.backbone_reuse_force_compute_steps
+        if args.backbone_reuse_force_compute_steps else None)
+    backbone_reuse_enabled = (
+        args.backbone_reuse_mode == "reuse_output"
+        and backbone_reuse_blocks is not None)
     if args.analysis_log_metrics:
         analysis_logger = InteractionAnalysisLogger(
             output_dir=inference_dir,
             psnr_lookup=load_psnr_lookup(args.analysis_psnr_path),
         )
     if args.analysis_profile_backbone_blocks:
-        if head_schedule is not None:
+        if head_schedule is not None or backbone_reuse_enabled:
             raise ValueError(
                 "Backbone block profiling expects dense DDIM runs. "
-                "Do not pass --head_schedule_steps.")
+                "Do not pass sparse head or backbone reuse flags.")
         backbone_profiler = BackboneBlockProfiler(output_dir=inference_dir)
     case_id = get_case_id(args.prompt_dir)
 
@@ -1233,7 +1250,7 @@ def run_inference(args: argparse.Namespace, gpu_num: int, gpu_no: int) -> None:
                     backbone_profiler.finish_pass()
                 need_policy_reference = args.analysis_log_metrics and (
                     args.analysis_reference_steps != args.ddim_steps
-                    or head_schedule is not None)
+                    or head_schedule is not None or backbone_reuse_enabled)
                 if need_policy_reference:
                     _, _, _, policy_reference_debug = image_guided_synthesis_sim_mode(
                         model,
@@ -1282,6 +1299,13 @@ def run_inference(args: argparse.Namespace, gpu_num: int, gpu_no: int) -> None:
                     head_schedule=head_schedule,
                     head_log_steps=head_log_steps,
                     head_skip_mode=head_skip_mode,
+                    backbone_reuse_blocks=backbone_reuse_blocks,
+                    backbone_reuse_start_step=args.backbone_reuse_start_step,
+                    backbone_reuse_schedule_steps=
+                    backbone_reuse_schedule_steps,
+                    backbone_reuse_force_compute_steps=
+                    backbone_reuse_force_compute_steps,
+                    backbone_reuse_mode=args.backbone_reuse_mode,
                     backbone_block_profiler=backbone_profiler)
                 if backbone_profiler is not None:
                     backbone_profiler.finish_pass()
@@ -1364,7 +1388,7 @@ def run_inference(args: argparse.Namespace, gpu_num: int, gpu_no: int) -> None:
                     backbone_profiler.finish_pass()
                 need_world_reference = args.analysis_log_metrics and (
                     args.analysis_reference_steps != args.ddim_steps
-                    or head_schedule is not None)
+                    or head_schedule is not None or backbone_reuse_enabled)
                 if need_world_reference:
                     _, _, _, world_reference_debug = image_guided_synthesis_sim_mode(
                         model,
@@ -1413,6 +1437,13 @@ def run_inference(args: argparse.Namespace, gpu_num: int, gpu_no: int) -> None:
                     head_schedule=head_schedule,
                     head_log_steps=head_log_steps,
                     head_skip_mode=head_skip_mode,
+                    backbone_reuse_blocks=backbone_reuse_blocks,
+                    backbone_reuse_start_step=args.backbone_reuse_start_step,
+                    backbone_reuse_schedule_steps=
+                    backbone_reuse_schedule_steps,
+                    backbone_reuse_force_compute_steps=
+                    backbone_reuse_force_compute_steps,
+                    backbone_reuse_mode=args.backbone_reuse_mode,
                     backbone_block_profiler=backbone_profiler)
                 if backbone_profiler is not None:
                     backbone_profiler.finish_pass()
@@ -1659,6 +1690,35 @@ def get_parser():
         default="reuse_prediction",
         choices=["reuse_prediction", "freeze_state"],
         help="Behavior on non-checkpoint steps: reuse cached head predictions while still running scheduler.step, or freeze action/state entirely.")
+    parser.add_argument(
+        "--backbone_reuse_blocks",
+        type=str,
+        nargs='*',
+        default=None,
+        help="Decoder block names to reuse on non-checkpoint DDIM steps, e.g. output_5 output_4 output_6.")
+    parser.add_argument(
+        "--backbone_reuse_start_step",
+        type=int,
+        default=None,
+        help="1-based DDIM step index after which decoder block reuse becomes eligible.")
+    parser.add_argument(
+        "--backbone_reuse_schedule_steps",
+        type=int,
+        nargs='*',
+        default=None,
+        help="1-based DDIM step indices where selected decoder blocks must be recomputed.")
+    parser.add_argument(
+        "--backbone_reuse_force_compute_steps",
+        type=int,
+        nargs='*',
+        default=None,
+        help="1-based DDIM step indices that always recompute selected decoder blocks, even if omitted from the reuse schedule.")
+    parser.add_argument(
+        "--backbone_reuse_mode",
+        type=str,
+        default="disabled",
+        choices=["disabled", "reuse_output"],
+        help="Decoder block reuse mode. 'reuse_output' reuses cached output block tensors on non-checkpoint steps.")
     return parser
 
 

scripts/run_backbone_reuse_worst_cases.sh

@@ -0,0 +1,20 @@
+#!/usr/bin/env bash
+
+set -euo pipefail
+
+repo_root="$(cd "$(dirname "${BASH_SOURCE[0]}")/.." && pwd)"
+cd "${repo_root}"
+
+case_scripts=(
+    "unitree_z1_dual_arm_stackbox/case1/run_world_model_backbone_reuse_output546.sh"
+    "unitree_z1_dual_arm_stackbox/case3/run_world_model_backbone_reuse_output546.sh"
+    "unitree_z1_dual_arm_stackbox_v2/case2/run_world_model_backbone_reuse_output546.sh"
+    "unitree_z1_dual_arm_stackbox_v2/case3/run_world_model_backbone_reuse_output546.sh"
+)
+
+for case_script in "${case_scripts[@]}"; do
+    echo "============================================================"
+    echo "Running ${case_script}"
+    echo "============================================================"
+    bash "${repo_root}/${case_script}"
+done

src/unifolm_wma/models/samplers/ddim.py

@@ -113,6 +113,11 @@ class DDIMSampler(object):
             head_schedule=None,
             head_log_steps=None,
             head_skip_mode="reuse_prediction",
+            backbone_reuse_blocks=None,
+            backbone_reuse_start_step=None,
+            backbone_reuse_schedule_steps=None,
+            backbone_reuse_force_compute_steps=None,
+            backbone_reuse_mode="disabled",
             **kwargs):
 
         # Check condition bs
@@ -174,6 +179,11 @@ class DDIMSampler(object):
             head_schedule=head_schedule,
             head_log_steps=head_log_steps,
             head_skip_mode=head_skip_mode,
+            backbone_reuse_blocks=backbone_reuse_blocks,
+            backbone_reuse_start_step=backbone_reuse_start_step,
+            backbone_reuse_schedule_steps=backbone_reuse_schedule_steps,
+            backbone_reuse_force_compute_steps=backbone_reuse_force_compute_steps,
+            backbone_reuse_mode=backbone_reuse_mode,
             **kwargs)
         return samples, actions, states, intermediates
 
@@ -206,6 +216,11 @@ class DDIMSampler(object):
                       head_schedule=None,
                       head_log_steps=None,
                       head_skip_mode="reuse_prediction",
+                      backbone_reuse_blocks=None,
+                      backbone_reuse_start_step=None,
+                      backbone_reuse_schedule_steps=None,
+                      backbone_reuse_force_compute_steps=None,
+                      backbone_reuse_mode="disabled",
                       **kwargs):
         device = self.model.betas.device
         dp_ddim_scheduler_action = self.model.dp_noise_scheduler_action
@@ -265,6 +280,25 @@ class DDIMSampler(object):
         }
         if head_log_steps_set is not None:
             intermediates['head_sparse_logs'] = {}
+        backbone_reuse_blocks_set = None if backbone_reuse_blocks is None else {
+            str(block_name) for block_name in backbone_reuse_blocks
+        }
+        backbone_reuse_schedule_steps_set = (
+            None if backbone_reuse_schedule_steps is None else {
+                int(step_index) for step_index in backbone_reuse_schedule_steps
+            })
+        backbone_reuse_force_compute_steps_set = (
+            None if backbone_reuse_force_compute_steps is None else {
+                int(step_index)
+                for step_index in backbone_reuse_force_compute_steps
+            })
+        backbone_reuse_active = (backbone_reuse_mode == "reuse_output"
+                                 and backbone_reuse_blocks_set)
+        backbone_reuse_cache = {
+            'single': {},
+            'cond': {},
+            'uncond': {},
+        }
         time_range = reversed(range(
             0, timesteps)) if ddim_use_original_steps else np.flip(timesteps)
         total_steps = timesteps if ddim_use_original_steps else timesteps.shape[
@@ -282,6 +316,10 @@ class DDIMSampler(object):
             raise ValueError(
                 f"Unsupported head_skip_mode={head_skip_mode!r}. "
                 "Expected 'reuse_prediction' or 'freeze_state'.")
+        if backbone_reuse_mode not in {"disabled", "reuse_output"}:
+            raise ValueError(
+                f"Unsupported backbone_reuse_mode={backbone_reuse_mode!r}. "
+                "Expected 'disabled' or 'reuse_output'.")
         x_action_frozen = action.detach().clone()
         x_state_frozen = state.detach().clone()
         action_pred_cache = None
@@ -310,6 +348,13 @@ class DDIMSampler(object):
                 run_head = scheduled_head or action_pred_cache is None or state_pred_cache is None
             else:
                 run_head = scheduled_head
+            backbone_reuse_step_stats: dict[str, set[str]] | None = None
+            if backbone_reuse_active:
+                backbone_reuse_step_stats = {
+                    'single': set(),
+                    'cond': set(),
+                    'uncond': set(),
+                }
 
             outs = self.p_sample_ddim(
                 img,
@@ -332,6 +377,14 @@ class DDIMSampler(object):
                 guidance_rescale=guidance_rescale,
                 backbone_step_index=i + 1,
                 run_head=run_head,
+                backbone_reuse_blocks=backbone_reuse_blocks_set,
+                backbone_reuse_start_step=backbone_reuse_start_step,
+                backbone_reuse_schedule_steps=backbone_reuse_schedule_steps_set,
+                backbone_reuse_force_compute_steps=
+                backbone_reuse_force_compute_steps_set,
+                backbone_reuse_mode=backbone_reuse_mode,
+                backbone_reuse_cache=backbone_reuse_cache,
+                backbone_reuse_step_stats=backbone_reuse_step_stats,
                 **kwargs)
 
             img, pred_x0, model_output_action, model_output_state = outs
@@ -380,6 +433,15 @@ class DDIMSampler(object):
             if callback: callback(i)
             if img_callback: img_callback(pred_x0, i)
 
+            reused_blocks = []
+            if backbone_reuse_step_stats is not None:
+                if unconditional_conditioning is None or unconditional_guidance_scale == 1.:
+                    reused_blocks = sorted(backbone_reuse_step_stats['single'])
+                else:
+                    reused_blocks = sorted(
+                        backbone_reuse_step_stats['cond']
+                        | backbone_reuse_step_stats['uncond'])
+
             if index % log_every_t == 0 or index == total_steps - 1:
                 intermediates['x_inter'].append(img)
                 intermediates['pred_x0'].append(pred_x0)
@@ -405,6 +467,8 @@ class DDIMSampler(object):
                     'action': action.detach().cpu(),
                     'state': state.detach().cpu(),
                     'step_time_s': step_time_s,
+                    'backbone_reused_blocks_count': len(reused_blocks),
+                    'backbone_reuse_hit_blocks': ",".join(reused_blocks),
                 })
 
         return img, action, state, intermediates
@@ -441,16 +505,34 @@ class DDIMSampler(object):
 
         if unconditional_conditioning is None or unconditional_guidance_scale == 1.:
             model_output, model_output_action, model_output_state = self.model.apply_model(
-                x, x_action, x_state, t, c, run_head=run_head,
+                x,
+                x_action,
+                x_state,
+                t,
+                c,
+                run_head=run_head,
+                backbone_reuse_branch="single",
                 **kwargs)  # unet denoiser
         else:
             # do_classifier_free_guidance
             if isinstance(c, torch.Tensor) or isinstance(c, dict):
                 e_t_cond, e_t_cond_action, e_t_cond_state = self.model.apply_model(
-                    x, x_action, x_state, t, c, run_head=run_head, **kwargs)
+                    x,
-                e_t_uncond, e_t_uncond_action, e_t_uncond_state = self.model.apply_model(
+                    x_action,
-                    x, x_action, x_state, t, unconditional_conditioning,
+                    x_state,
+                    t,
+                    c,
                     run_head=run_head,
+                    backbone_reuse_branch="cond",
+                    **kwargs)
+                e_t_uncond, e_t_uncond_action, e_t_uncond_state = self.model.apply_model(
+                    x,
+                    x_action,
+                    x_state,
+                    t,
+                    unconditional_conditioning,
+                    run_head=run_head,
+                    backbone_reuse_branch="uncond",
                     **kwargs)
             else:
                 raise NotImplementedError

src/unifolm_wma/modules/networks/wma_model.py

@@ -719,6 +719,18 @@ class WMAModel(nn.Module):
         run_head = kwargs.pop("run_head", True)
         backbone_block_profiler = kwargs.pop("backbone_block_profiler", None)
         backbone_step_index = kwargs.pop("backbone_step_index", None)
+        backbone_reuse_blocks = kwargs.pop("backbone_reuse_blocks", None)
+        backbone_reuse_start_step = kwargs.pop("backbone_reuse_start_step",
+                                               None)
+        backbone_reuse_schedule_steps = kwargs.pop(
+            "backbone_reuse_schedule_steps", None)
+        backbone_reuse_force_compute_steps = kwargs.pop(
+            "backbone_reuse_force_compute_steps", None)
+        backbone_reuse_mode = kwargs.pop("backbone_reuse_mode", "disabled")
+        backbone_reuse_cache = kwargs.pop("backbone_reuse_cache", None)
+        backbone_reuse_step_stats = kwargs.pop("backbone_reuse_step_stats",
+                                               None)
+        backbone_reuse_branch = kwargs.pop("backbone_reuse_branch", "single")
         t_emb = timestep_embedding(timesteps,
                                    self.model_channels,
                                    repeat_only=False).type(x.dtype)
@@ -816,6 +828,32 @@ class WMAModel(nn.Module):
             )
             return out
 
+        reuse_cache_branch: Dict[str, Tensor] | None = None
+        if backbone_reuse_cache is not None:
+            reuse_cache_branch = backbone_reuse_cache.setdefault(
+                backbone_reuse_branch, {})
+
+        def should_reuse_output_block(block_name: str) -> bool:
+            if backbone_reuse_mode != "reuse_output":
+                return False
+            if backbone_step_index is None or backbone_reuse_start_step is None:
+                return False
+            if backbone_reuse_blocks is None or block_name not in backbone_reuse_blocks:
+                return False
+            if int(backbone_step_index) < int(backbone_reuse_start_step):
+                return False
+            if (backbone_reuse_force_compute_steps is not None
+                    and int(backbone_step_index)
+                    in backbone_reuse_force_compute_steps):
+                return False
+            if (backbone_reuse_schedule_steps is not None
+                    and int(backbone_step_index)
+                    in backbone_reuse_schedule_steps):
+                return False
+            if reuse_cache_branch is None:
+                return False
+            return block_name in reuse_cache_branch
+
         h = x.type(self.dtype)
         adapter_idx = 0
         hs = []
@@ -861,6 +899,7 @@ class WMAModel(nn.Module):
         hs_out = []
         for id, module in enumerate(self.output_blocks):
             skip_h = hs.pop()
+            block_name = f"output_{id}"
 
             def run_output_block() -> Tensor:
                 return module(torch.cat([h, skip_h], dim=1),
@@ -868,12 +907,24 @@ class WMAModel(nn.Module):
                               context=context,
                               batch_size=b)
 
+            if should_reuse_output_block(block_name):
+                h = reuse_cache_branch[block_name].to(device=h.device,
+                                                      dtype=h.dtype)
+                if backbone_reuse_step_stats is not None:
+                    backbone_reuse_step_stats.setdefault(
+                        backbone_reuse_branch, set()).add(block_name)
+            else:
                 h = run_block_with_profile(
-                block_name=f"output_{id}",
+                    block_name=block_name,
                     block_stage="output_blocks",
                     block_index=id,
                     fn=run_output_block,
                 )
+                if (reuse_cache_branch is not None and backbone_reuse_mode ==
+                        "reuse_output"
+                        and backbone_reuse_blocks is not None
+                        and block_name in backbone_reuse_blocks):
+                    reuse_cache_branch[block_name] = h.detach().clone()
             if isinstance(module[-1], Upsample):
                 hs_a.append(
                     rearrange(hs_out[-1], '(b t) c h w -> b t c h w', t=t))

unitree_z1_dual_arm_stackbox/case1/output/backbone_reuse_output546_tail_sparse_8/inference/round_summary.csv

@@ -0,0 +1,8 @@
+sample_id,scene,round_id,policy_pass_total_time_s,world_model_pass_total_time_s,round_total_time_s,latent_init_dist_to_prev_round,action_drift_vs_prev_round,psnr_full50
+unitree_z1_dual_arm_stackbox-vid5-fs4,unitree_z1_dual_arm_stackbox,0,34.60245490074158,34.72423195838928,138.95395731925964,,,
+unitree_z1_dual_arm_stackbox-vid5-fs4,unitree_z1_dual_arm_stackbox,1,34.59635591506958,34.85775184631348,139.0369472503662,211.345458984375,0.4255753755569458,
+unitree_z1_dual_arm_stackbox-vid5-fs4,unitree_z1_dual_arm_stackbox,2,34.56254506111145,34.743552446365356,138.9944486618042,211.6240234375,0.7389801144599915,
+unitree_z1_dual_arm_stackbox-vid5-fs4,unitree_z1_dual_arm_stackbox,3,34.85900378227234,34.58891749382019,139.22013521194458,218.88330078125,0.4218013286590576,
+unitree_z1_dual_arm_stackbox-vid5-fs4,unitree_z1_dual_arm_stackbox,4,34.56788682937622,34.80677080154419,139.0237329006195,182.1666259765625,0.2393476366996765,
+unitree_z1_dual_arm_stackbox-vid5-fs4,unitree_z1_dual_arm_stackbox,5,34.56533694267273,34.626118183135986,138.74371457099915,206.19061279296875,0.33043384552001953,
+unitree_z1_dual_arm_stackbox-vid5-fs4,unitree_z1_dual_arm_stackbox,6,34.5905020236969,34.810385942459106,138.9610471725464,159.6436309814453,0.36647987365722656,

unitree_z1_dual_arm_stackbox/case1/output/backbone_reuse_output546_tail_sparse_8/inference/sample_summary.csv

@@ -0,0 +1,3 @@
+sample_id,scene,pass_type,pass_total_time_s,action_first_stable_step,state_first_stable_step,latent_first_stable_step,action_vs_full50_90pct_step,action_vs_full50_95pct_step,oracle_budget_action,oracle_budget_state,oracle_budget_latent,latent_init_dist_to_prev_round,action_drift_vs_prev_round,round_total_time_s,policy_pass_total_time_s,world_model_pass_total_time_s,psnr_full50
+unitree_z1_dual_arm_stackbox-vid5-fs4,unitree_z1_dual_arm_stackbox,policy,34.620583636420115,,,,46.8,48.75,47.42857142857143,48.0,,198.30894215901694,0.42043636242548627,138.99056901250566,34.620583636420115,34.73681838171823,
+unitree_z1_dual_arm_stackbox-vid5-fs4,unitree_z1_dual_arm_stackbox,world_model,34.73681838171823,,,,47.0,48.75,47.285714285714285,47.714285714285715,,198.30894215901694,0.42043636242548627,138.99056901250566,34.620583636420115,34.73681838171823,

unitree_z1_dual_arm_stackbox/case1/output/backbone_reuse_output546_tail_sparse_8/inference/stepwise_log.csv

@@ -0,0 +1,701 @@
+sample_id,scene,pass_type,round_id,step,step_time_s,backbone_reused_blocks_count,backbone_reuse_hit_blocks,latent_delta,action_delta,state_delta,action_cosine_vs_full50,state_cosine_vs_full50,latent_l2_vs_full50
+unitree_z1_dual_arm_stackbox-vid5-fs4,unitree_z1_dual_arm_stackbox,policy,0,1,0.6848506927490234,0,,1.4160528182983398,0.07500046491622925,0.07889977097511292,0.11988209933042526,0.06478386372327805,167.1123046875
+unitree_z1_dual_arm_stackbox-vid5-fs4,unitree_z1_dual_arm_stackbox,policy,0,2,0.6846530437469482,0,,1.0183463096618652,0.0540202371776104,0.046346213668584824,0.1111149936914444,0.0605180561542511,167.2060546875
+unitree_z1_dual_arm_stackbox-vid5-fs4,unitree_z1_dual_arm_stackbox,policy,0,3,0.6857845783233643,0,,0.8496554493904114,0.04339855909347534,0.02831621654331684,0.10775137692689896,0.06171846017241478,164.61880493164062
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+unitree_z1_dual_arm_stackbox-vid5-fs4,unitree_z1_dual_arm_stackbox,world_model,6,50,0.6931014060974121,0,,0.12212962657213211,0.12339522689580917,0.139326274394989,0.9334924221038818,0.9892199039459229,132.0954132080078

unitree_z1_dual_arm_stackbox/case1/run_world_model_backbone_reuse_output546.sh

@@ -0,0 +1,31 @@
+res_dir="unitree_z1_dual_arm_stackbox/case1"
+dataset="unitree_z1_dual_arm_stackbox"
+
+{
+    time CUDA_VISIBLE_DEVICES=0 "${PYTHON_BIN:-python}" scripts/evaluation/world_model_interaction.py \
+        --seed 123 \
+        --ckpt_path ckpts/unifolm_wma_dual.ckpt \
+        --config configs/inference/world_model_interaction.yaml \
+        --savedir "${res_dir}/output/backbone_reuse_output546_tail_sparse_8" \
+        --bs 1 --height 320 --width 512 \
+        --unconditional_guidance_scale 1.0 \
+        --ddim_steps 50 \
+        --ddim_eta 1.0 \
+        --prompt_dir "unitree_z1_dual_arm_stackbox/case1/world_model_interaction_prompts" \
+        --dataset ${dataset} \
+        --video_length 16 \
+        --frame_stride 4 \
+        --n_action_steps 16 \
+        --exe_steps 16 \
+        --n_iter 7 \
+        --timestep_spacing 'uniform_trailing' \
+        --guidance_rescale 0.7 \
+        --perframe_ae \
+        --analysis_log_metrics \
+        --analysis_reference_steps 50 \
+        --backbone_reuse_mode reuse_output \
+        --backbone_reuse_blocks output_5 output_4 output_6 \
+        --backbone_reuse_start_step 35 \
+        --backbone_reuse_schedule_steps 35 37 39 41 43 45 47 49 \
+        --backbone_reuse_force_compute_steps 50
+} 2>&1 | tee "${res_dir}/output_backbone_reuse_output546_tail_sparse_8.log"

unitree_z1_dual_arm_stackbox/case3/run_world_model_backbone_reuse_output546.sh

@@ -0,0 +1,31 @@
+res_dir="unitree_z1_dual_arm_stackbox/case3"
+dataset="unitree_z1_dual_arm_stackbox"
+
+{
+    time CUDA_VISIBLE_DEVICES=0 "${PYTHON_BIN:-python}" scripts/evaluation/world_model_interaction.py \
+        --seed 123 \
+        --ckpt_path ckpts/unifolm_wma_dual.ckpt \
+        --config configs/inference/world_model_interaction.yaml \
+        --savedir "${res_dir}/output/backbone_reuse_output546_tail_sparse_8" \
+        --bs 1 --height 320 --width 512 \
+        --unconditional_guidance_scale 1.0 \
+        --ddim_steps 50 \
+        --ddim_eta 1.0 \
+        --prompt_dir "unitree_z1_dual_arm_stackbox/case3/world_model_interaction_prompts" \
+        --dataset ${dataset} \
+        --video_length 16 \
+        --frame_stride 4 \
+        --n_action_steps 16 \
+        --exe_steps 16 \
+        --n_iter 7 \
+        --timestep_spacing 'uniform_trailing' \
+        --guidance_rescale 0.7 \
+        --perframe_ae \
+        --analysis_log_metrics \
+        --analysis_reference_steps 50 \
+        --backbone_reuse_mode reuse_output \
+        --backbone_reuse_blocks output_5 output_4 output_6 \
+        --backbone_reuse_start_step 35 \
+        --backbone_reuse_schedule_steps 35 37 39 41 43 45 47 49 \
+        --backbone_reuse_force_compute_steps 50
+} 2>&1 | tee "${res_dir}/output_backbone_reuse_output546_tail_sparse_8.log"

unitree_z1_dual_arm_stackbox_v2/case2/run_world_model_backbone_reuse_output546.sh

@@ -0,0 +1,31 @@
+res_dir="unitree_z1_dual_arm_stackbox_v2/case2"
+dataset="unitree_z1_dual_arm_stackbox_v2"
+
+{
+    time CUDA_VISIBLE_DEVICES=0 "${PYTHON_BIN:-python}" scripts/evaluation/world_model_interaction.py \
+        --seed 123 \
+        --ckpt_path ckpts/unifolm_wma_dual.ckpt \
+        --config configs/inference/world_model_interaction.yaml \
+        --savedir "${res_dir}/output/backbone_reuse_output546_tail_sparse_8" \
+        --bs 1 --height 320 --width 512 \
+        --unconditional_guidance_scale 1.0 \
+        --ddim_steps 50 \
+        --ddim_eta 1.0 \
+        --prompt_dir "unitree_z1_dual_arm_stackbox_v2/case2/world_model_interaction_prompts" \
+        --dataset ${dataset} \
+        --video_length 16 \
+        --frame_stride 4 \
+        --n_action_steps 16 \
+        --exe_steps 16 \
+        --n_iter 11 \
+        --timestep_spacing 'uniform_trailing' \
+        --guidance_rescale 0.7 \
+        --perframe_ae \
+        --analysis_log_metrics \
+        --analysis_reference_steps 50 \
+        --backbone_reuse_mode reuse_output \
+        --backbone_reuse_blocks output_5 output_4 output_6 \
+        --backbone_reuse_start_step 35 \
+        --backbone_reuse_schedule_steps 35 37 39 41 43 45 47 49 \
+        --backbone_reuse_force_compute_steps 50
+} 2>&1 | tee "${res_dir}/output_backbone_reuse_output546_tail_sparse_8.log"

unitree_z1_dual_arm_stackbox_v2/case3/run_world_model_backbone_reuse_output546.sh

@@ -0,0 +1,31 @@
+res_dir="unitree_z1_dual_arm_stackbox_v2/case3"
+dataset="unitree_z1_dual_arm_stackbox_v2"
+
+{
+    time CUDA_VISIBLE_DEVICES=0 "${PYTHON_BIN:-python}" scripts/evaluation/world_model_interaction.py \
+        --seed 123 \
+        --ckpt_path ckpts/unifolm_wma_dual.ckpt \
+        --config configs/inference/world_model_interaction.yaml \
+        --savedir "${res_dir}/output/backbone_reuse_output546_tail_sparse_8" \
+        --bs 1 --height 320 --width 512 \
+        --unconditional_guidance_scale 1.0 \
+        --ddim_steps 50 \
+        --ddim_eta 1.0 \
+        --prompt_dir "unitree_z1_dual_arm_stackbox_v2/case3/world_model_interaction_prompts" \
+        --dataset ${dataset} \
+        --video_length 16 \
+        --frame_stride 4 \
+        --n_action_steps 16 \
+        --exe_steps 16 \
+        --n_iter 11 \
+        --timestep_spacing 'uniform_trailing' \
+        --guidance_rescale 0.7 \
+        --perframe_ae \
+        --analysis_log_metrics \
+        --analysis_reference_steps 50 \
+        --backbone_reuse_mode reuse_output \
+        --backbone_reuse_blocks output_5 output_4 output_6 \
+        --backbone_reuse_start_step 35 \
+        --backbone_reuse_schedule_steps 35 37 39 41 43 45 47 49 \
+        --backbone_reuse_force_compute_steps 50
+} 2>&1 | tee "${res_dir}/output_backbone_reuse_output546_tail_sparse_8.log"

unitree_z1_stackbox/case3/output/backbone_reuse_output546_tail_sparse_8/inference/round_summary.csv

@@ -0,0 +1,13 @@
+sample_id,scene,round_id,policy_pass_total_time_s,world_model_pass_total_time_s,round_total_time_s,latent_init_dist_to_prev_round,action_drift_vs_prev_round,psnr_full50
+unitree_z1_stackbox-vid25-fs4,unitree_z1_stackbox,0,34.61543416976929,35.12265110015869,140.12255382537842,,,
+unitree_z1_stackbox-vid25-fs4,unitree_z1_stackbox,1,34.92478108406067,34.903425455093384,140.50326132774353,238.71011352539062,0.1955518126487732,
+unitree_z1_stackbox-vid25-fs4,unitree_z1_stackbox,2,34.93473482131958,34.86671566963196,140.23696851730347,193.00128173828125,0.22892463207244873,
+unitree_z1_stackbox-vid25-fs4,unitree_z1_stackbox,3,34.8467276096344,34.840471267700195,139.95978212356567,203.62391662597656,0.2653714418411255,
+unitree_z1_stackbox-vid25-fs4,unitree_z1_stackbox,4,34.84385108947754,34.884554386138916,140.10617589950562,175.98419189453125,0.2676825523376465,
+unitree_z1_stackbox-vid25-fs4,unitree_z1_stackbox,5,34.84644269943237,34.84303593635559,140.06873226165771,177.170654296875,0.20933383703231812,
+unitree_z1_stackbox-vid25-fs4,unitree_z1_stackbox,6,34.842782735824585,34.959144592285156,140.20916867256165,158.33082580566406,0.3230288028717041,
+unitree_z1_stackbox-vid25-fs4,unitree_z1_stackbox,7,34.8296320438385,35.06740975379944,140.3599042892456,186.1202392578125,0.29313647747039795,
+unitree_z1_stackbox-vid25-fs4,unitree_z1_stackbox,8,34.718727111816406,34.513421297073364,139.18176984786987,176.69534301757812,0.2630300521850586,
+unitree_z1_stackbox-vid25-fs4,unitree_z1_stackbox,9,34.467792987823486,34.49754571914673,138.48231291770935,207.1377716064453,0.27973079681396484,
+unitree_z1_stackbox-vid25-fs4,unitree_z1_stackbox,10,34.49251866340637,34.52156186103821,138.64975929260254,167.4407501220703,0.17017686367034912,
+unitree_z1_stackbox-vid25-fs4,unitree_z1_stackbox,11,34.500508069992065,34.50804042816162,138.59093475341797,171.18487548828125,0.16435915231704712,

unitree_z1_stackbox/case3/output/backbone_reuse_output546_tail_sparse_8/inference/sample_summary.csv

@@ -0,0 +1,3 @@
+sample_id,scene,pass_type,pass_total_time_s,action_first_stable_step,state_first_stable_step,latent_first_stable_step,action_vs_full50_90pct_step,action_vs_full50_95pct_step,oracle_budget_action,oracle_budget_state,oracle_budget_latent,latent_init_dist_to_prev_round,action_drift_vs_prev_round,round_total_time_s,policy_pass_total_time_s,world_model_pass_total_time_s,psnr_full50
+unitree_z1_stackbox-vid25-fs4,unitree_z1_stackbox,policy,34.738661090532936,,,,46.25,48.0,47.666666666666664,48.333333333333336,,186.85454212535512,0.2418478564782576,139.70594364404678,34.738661090532936,34.79399812221527,
+unitree_z1_stackbox-vid25-fs4,unitree_z1_stackbox,world_model,34.79399812221527,,,,46.333333333333336,48.083333333333336,47.75,48.25,,186.85454212535512,0.2418478564782576,139.70594364404678,34.738661090532936,34.79399812221527,

unitree_z1_stackbox/case3/run_world_model_backbone_reuse_output546.sh

@@ -0,0 +1,31 @@
+res_dir="unitree_z1_stackbox/case3"
+dataset="unitree_z1_stackbox"
+
+{
+    time CUDA_VISIBLE_DEVICES=0 "${PYTHON_BIN:-python}" scripts/evaluation/world_model_interaction.py \
+        --seed 123 \
+        --ckpt_path ckpts/unifolm_wma_dual.ckpt \
+        --config configs/inference/world_model_interaction.yaml \
+        --savedir "${res_dir}/output/backbone_reuse_output546_tail_sparse_8" \
+        --bs 1 --height 320 --width 512 \
+        --unconditional_guidance_scale 1.0 \
+        --ddim_steps 50 \
+        --ddim_eta 1.0 \
+        --prompt_dir "unitree_z1_stackbox/case3/world_model_interaction_prompts" \
+        --dataset ${dataset} \
+        --video_length 16 \
+        --frame_stride 4 \
+        --n_action_steps 16 \
+        --exe_steps 16 \
+        --n_iter 12 \
+        --timestep_spacing 'uniform_trailing' \
+        --guidance_rescale 0.7 \
+        --perframe_ae \
+        --analysis_log_metrics \
+        --analysis_reference_steps 50 \
+        --backbone_reuse_mode reuse_output \
+        --backbone_reuse_blocks output_5 output_4 output_6 \
+        --backbone_reuse_start_step 35 \
+        --backbone_reuse_schedule_steps 35 37 39 41 43 45 47 49 \
+        --backbone_reuse_force_compute_steps 50
+} 2>&1 | tee "${res_dir}/output_backbone_reuse_output546_tail_sparse_8.log"