结果

- world model 调用 decode_video=False，跳过 16 帧全量 decode
- 只 decode 最后 1 帧给 CLIP embedding / observation queue
- 存 raw latent，循环结束后统一 batch decode 生成最终视频
- 每轮省 15 次 VAE decode，8 轮共省 120 次
- 跳过中间迭代的 wm tensorboard/mp4 保存

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>

scripts/evaluation/world_model_interaction.py

@@ -559,6 +559,7 @@ def image_guided_synthesis_sim_mode(
         autocast_ctx = nullcontext()
 
     batch_variants = None
+    samples = None
     if ddim_sampler is not None:
         with autocast_ctx:
             samples, actions, states, intermedia = ddim_sampler.sample(
@@ -583,7 +584,7 @@ def image_guided_synthesis_sim_mode(
             batch_images = model.decode_first_stage(samples)
             batch_variants = batch_images
 
-    return batch_variants, actions, states
+    return batch_variants, actions, states, samples
 
 
 def run_inference(args: argparse.Namespace, gpu_num: int, gpu_no: int) -> None:
@@ -625,6 +626,12 @@ def run_inference(args: argparse.Namespace, gpu_num: int, gpu_no: int) -> None:
     # Compile hot ResBlocks for operator fusion
     apply_torch_compile(model)
 
+    # Fuse KV projections in attention layers (to_k + to_v → to_kv)
+    from unifolm_wma.modules.attention import CrossAttention
+    kv_count = sum(1 for m in model.modules()
+                   if isinstance(m, CrossAttention) and m.fuse_kv())
+    print(f"    ✓ KV fused: {kv_count} attention layers")
+
     # Export precision-converted checkpoint if requested
     if args.export_precision_ckpt:
         export_path = args.export_precision_ckpt
@@ -687,7 +694,7 @@ def run_inference(args: argparse.Namespace, gpu_num: int, gpu_no: int) -> None:
             sample_save_dir = f'{video_save_dir}/wm/{fs}'
             os.makedirs(sample_save_dir, exist_ok=True)
             # For collecting interaction videos
-            wm_video = []
+            wm_latent = []
             # Initialize observation queues
             cond_obs_queues = {
                 "observation.images.top":
@@ -743,7 +750,7 @@ def run_inference(args: argparse.Namespace, gpu_num: int, gpu_no: int) -> None:
 
                 # Use world-model in policy to generate action
                 print(f'>>> Step {itr}: generating actions ...')
-                pred_videos_0, pred_actions, _ = image_guided_synthesis_sim_mode(
+                pred_videos_0, pred_actions, _, _ = image_guided_synthesis_sim_mode(
                     model,
                     sample['instruction'],
                     observation,
@@ -785,7 +792,7 @@ def run_inference(args: argparse.Namespace, gpu_num: int, gpu_no: int) -> None:
 
                 # Interaction with the world-model
                 print(f'>>> Step {itr}: interacting with world model ...')
-                pred_videos_1, _, pred_states = image_guided_synthesis_sim_mode(
+                pred_videos_1, _, pred_states, wm_samples = image_guided_synthesis_sim_mode(
                     model,
                     "",
                     observation,
@@ -798,12 +805,16 @@ def run_inference(args: argparse.Namespace, gpu_num: int, gpu_no: int) -> None:
                     fs=model_input_fs,
                     text_input=False,
                     timestep_spacing=args.timestep_spacing,
-                    guidance_rescale=args.guidance_rescale)
+                    guidance_rescale=args.guidance_rescale,
+                    decode_video=False)
+
+                # Decode only the last frame for CLIP embedding in next iteration
+                last_frame_pixel = model.decode_first_stage(wm_samples[:, :, -1:, :, :])
 
                 for idx in range(args.exe_steps):
                     observation = {
                         'observation.images.top':
-                        pred_videos_1[0][:, idx:idx + 1].permute(1, 0, 2, 3),
+                        last_frame_pixel[0, :, 0:1].permute(1, 0, 2, 3),
                         'observation.state':
                         torch.zeros_like(pred_states[0][idx:idx + 1]) if
                         args.zero_pred_state else pred_states[0][idx:idx + 1],
@@ -821,30 +832,14 @@ def run_inference(args: argparse.Namespace, gpu_num: int, gpu_no: int) -> None:
                                        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,
-                                   pred_videos_1,
-                                   sample_tag,
-                                   fps=args.save_fps)
-
-                # Save the imagen videos for decision-making
-                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(),
-                             sample_video_file,
-                             fps=args.save_fps)
 
                 print('>' * 24)
-                # Collect the result of world-model interactions
+                # Store raw latent for deferred decode
-                wm_video.append(pred_videos_1[:, :, :args.exe_steps].cpu())
+                wm_latent.append(wm_samples[:, :, :args.exe_steps].cpu())
 
-            full_video = torch.cat(wm_video, dim=2)
+            # Deferred decode: batch decode all stored latents
+            full_latent = torch.cat(wm_latent, dim=2).to(device)
+            full_video = model.decode_first_stage(full_latent).cpu()
             sample_tag = f"{args.dataset}-vid{sample['videoid']}-wd-fs-{fs}/full"
             log_to_tensorboard(writer,
                                full_video,

src/unifolm_wma/models/diffusion_head/conditional_unet1d.py

@@ -567,6 +567,11 @@ class ConditionalUnet1D(nn.Module):
         # Broadcast to batch dimension in a way that's compatible with ONNX/Core ML
         timesteps = timesteps.expand(sample.shape[0])
         global_feature = self.diffusion_step_encoder(timesteps)
+        # Pre-expand global_feature once (reused in every down/mid/up block)
+        if self.use_linear_act_proj:
+            global_feature_expanded = global_feature.unsqueeze(1).expand(-1, T, -1)
+        else:
+            global_feature_expanded = global_feature.unsqueeze(1).expand(-1, 2, -1)
         (imagen_cond_down, imagen_cond_mid, imagen_cond_up
          ) = imagen_cond[0:4], imagen_cond[4], imagen_cond[5:]  #NOTE HAND CODE
 
@@ -603,15 +608,11 @@ class ConditionalUnet1D(nn.Module):
 
             if self.use_linear_act_proj:
                 imagen_cond = imagen_cond.reshape(B, T, -1)
-                cur_global_feature = global_feature.unsqueeze(
-                    1).repeat_interleave(repeats=T, dim=1)
             else:
                 imagen_cond = imagen_cond.permute(0, 3, 1, 2)
                 imagen_cond = imagen_cond.reshape(B, 2, -1)
-                cur_global_feature = global_feature.unsqueeze(
-                    1).repeat_interleave(repeats=2, dim=1)
             cur_global_feature = torch.cat(
-                [cur_global_feature, global_cond, imagen_cond], axis=-1)
+                [global_feature_expanded, global_cond, imagen_cond], axis=-1)
             x = resnet(x, cur_global_feature)
             x = resnet2(x, cur_global_feature)
             h.append(x)
@@ -638,15 +639,11 @@ class ConditionalUnet1D(nn.Module):
         imagen_cond = rearrange(imagen_cond, '(b t) c d -> b t c d', b=B)
         if self.use_linear_act_proj:
             imagen_cond = imagen_cond.reshape(B, T, -1)
-            cur_global_feature = global_feature.unsqueeze(1).repeat_interleave(
-                repeats=T, dim=1)
         else:
             imagen_cond = imagen_cond.permute(0, 3, 1, 2)
             imagen_cond = imagen_cond.reshape(B, 2, -1)
-            cur_global_feature = global_feature.unsqueeze(1).repeat_interleave(
-                repeats=2, dim=1)
         cur_global_feature = torch.cat(
-            [cur_global_feature, global_cond, imagen_cond], axis=-1)
+            [global_feature_expanded, global_cond, imagen_cond], axis=-1)
         x = resnet(x, cur_global_feature)
         x = resnet2(x, cur_global_feature)
 
@@ -683,16 +680,12 @@ class ConditionalUnet1D(nn.Module):
 
             if self.use_linear_act_proj:
                 imagen_cond = imagen_cond.reshape(B, T, -1)
-                cur_global_feature = global_feature.unsqueeze(
-                    1).repeat_interleave(repeats=T, dim=1)
             else:
                 imagen_cond = imagen_cond.permute(0, 3, 1, 2)
                 imagen_cond = imagen_cond.reshape(B, 2, -1)
-                cur_global_feature = global_feature.unsqueeze(
-                    1).repeat_interleave(repeats=2, dim=1)
 
             cur_global_feature = torch.cat(
-                [cur_global_feature, global_cond, imagen_cond], axis=-1)
+                [global_feature_expanded, global_cond, imagen_cond], axis=-1)
 
             x = torch.cat((x, h.pop()), dim=1)
             x = resnet(x, cur_global_feature)

src/unifolm_wma/models/samplers/ddim.py

@@ -251,6 +251,13 @@ class DDIMSampler(object):
         dp_ddim_scheduler_action.set_timesteps(len(timesteps))
         dp_ddim_scheduler_state.set_timesteps(len(timesteps))
         ts = torch.empty((b, ), device=device, dtype=torch.long)
+        noise_buf = torch.empty_like(img)
+        # Pre-convert schedule arrays to inference dtype (avoid per-step .to())
+        _dtype = img.dtype
+        _alphas = (self.model.alphas_cumprod if ddim_use_original_steps else self.ddim_alphas).to(_dtype)
+        _alphas_prev = (self.model.alphas_cumprod_prev if ddim_use_original_steps else self.ddim_alphas_prev).to(_dtype)
+        _sqrt_one_minus = (self.model.sqrt_one_minus_alphas_cumprod if ddim_use_original_steps else self.ddim_sqrt_one_minus_alphas).to(_dtype)
+        _sigmas = (self.ddim_sigmas_for_original_num_steps if ddim_use_original_steps else self.ddim_sigmas).to(_dtype)
         enable_cross_attn_kv_cache(self.model)
         enable_ctx_cache(self.model)
         try:
@@ -286,6 +293,8 @@ class DDIMSampler(object):
                     x0=x0,
                     fs=fs,
                     guidance_rescale=guidance_rescale,
+                    noise_buf=noise_buf,
+                    schedule_arrays=(_alphas, _alphas_prev, _sqrt_one_minus, _sigmas),
                     **kwargs)
 
                 img, pred_x0, model_output_action, model_output_state = outs
@@ -339,6 +348,8 @@ class DDIMSampler(object):
                       mask=None,
                       x0=None,
                       guidance_rescale=0.0,
+                      noise_buf=None,
+                      schedule_arrays=None,
                       **kwargs):
         b, *_, device = *x.shape, x.device
 
@@ -384,16 +395,18 @@ class DDIMSampler(object):
             e_t = score_corrector.modify_score(self.model, e_t, x, t, c,
                                                **corrector_kwargs)
 
-        alphas = self.model.alphas_cumprod if use_original_steps else self.ddim_alphas
+        if schedule_arrays is not None:
-        alphas_prev = self.model.alphas_cumprod_prev if use_original_steps else self.ddim_alphas_prev
+            alphas, alphas_prev, sqrt_one_minus_alphas, sigmas = schedule_arrays
-        sqrt_one_minus_alphas = self.model.sqrt_one_minus_alphas_cumprod if use_original_steps else self.ddim_sqrt_one_minus_alphas
+        else:
-        sigmas = self.ddim_sigmas_for_original_num_steps if use_original_steps else self.ddim_sigmas
+            alphas = (self.model.alphas_cumprod if use_original_steps else self.ddim_alphas).to(x.dtype)
+            alphas_prev = (self.model.alphas_cumprod_prev if use_original_steps else self.ddim_alphas_prev).to(x.dtype)
+            sqrt_one_minus_alphas = (self.model.sqrt_one_minus_alphas_cumprod if use_original_steps else self.ddim_sqrt_one_minus_alphas).to(x.dtype)
+            sigmas = (self.ddim_sigmas_for_original_num_steps if use_original_steps else self.ddim_sigmas).to(x.dtype)
 
-        # Use 0-d tensors directly (already on device); broadcasting handles shape
+        a_t = alphas[index]
-        a_t = alphas[index].to(x.dtype)
+        a_prev = alphas_prev[index]
-        a_prev = alphas_prev[index].to(x.dtype)
+        sigma_t = sigmas[index]
-        sigma_t = sigmas[index].to(x.dtype)
+        sqrt_one_minus_at = sqrt_one_minus_alphas[index]
-        sqrt_one_minus_at = sqrt_one_minus_alphas[index].to(x.dtype)
 
         if self.model.parameterization != "v":
             pred_x0 = (x - sqrt_one_minus_at * e_t) / a_t.sqrt()
@@ -411,6 +424,10 @@ class DDIMSampler(object):
 
         dir_xt = (1. - a_prev - sigma_t**2).sqrt() * e_t
 
+        if noise_buf is not None:
+            noise_buf.normal_()
+            noise = sigma_t * noise_buf * temperature
+        else:
             noise = sigma_t * noise_like(x.shape, device,
                                          repeat_noise) * temperature
         if noise_dropout > 0.:

src/unifolm_wma/modules/attention.py

@@ -99,6 +99,7 @@ class CrossAttention(nn.Module):
         self.agent_action_context_len = agent_action_context_len
         self._kv_cache = {}
         self._kv_cache_enabled = False
+        self._kv_fused = False
 
         self.cross_attention_scale_learnable = cross_attention_scale_learnable
         if self.image_cross_attention:
@@ -116,6 +117,27 @@ class CrossAttention(nn.Module):
                 self.register_parameter('alpha_caa',
                                         nn.Parameter(torch.tensor(0.)))
 
+    def fuse_kv(self):
+        """Fuse to_k/to_v into to_kv (2 Linear → 1). Works for all layers."""
+        k_w = self.to_k.weight  # (inner_dim, context_dim)
+        v_w = self.to_v.weight
+        self.to_kv = nn.Linear(k_w.shape[1], k_w.shape[0] * 2, bias=False)
+        self.to_kv.weight = nn.Parameter(torch.cat([k_w, v_w], dim=0))
+        del self.to_k, self.to_v
+        if self.image_cross_attention:
+            for suffix in ('_ip', '_as', '_aa'):
+                k_attr = f'to_k{suffix}'
+                v_attr = f'to_v{suffix}'
+                kw = getattr(self, k_attr).weight
+                vw = getattr(self, v_attr).weight
+                fused = nn.Linear(kw.shape[1], kw.shape[0] * 2, bias=False)
+                fused.weight = nn.Parameter(torch.cat([kw, vw], dim=0))
+                setattr(self, f'to_kv{suffix}', fused)
+                delattr(self, k_attr)
+                delattr(self, v_attr)
+        self._kv_fused = True
+        return True
+
     def forward(self, x, context=None, mask=None):
         spatial_self_attn = (context is None)
         k_ip, v_ip, out_ip = None, None, None
@@ -276,6 +298,12 @@ class CrossAttention(nn.Module):
                                     self.agent_action_context_len +
                                     self.text_context_len:, :]
 
+            if self._kv_fused:
+                k, v = self.to_kv(context_ins).chunk(2, dim=-1)
+                k_ip, v_ip = self.to_kv_ip(context_image).chunk(2, dim=-1)
+                k_as, v_as = self.to_kv_as(context_agent_state).chunk(2, dim=-1)
+                k_aa, v_aa = self.to_kv_aa(context_agent_action).chunk(2, dim=-1)
+            else:
                 k = self.to_k(context_ins)
                 v = self.to_v(context_ins)
                 k_ip = self.to_k_ip(context_image)
@@ -304,6 +332,9 @@ class CrossAttention(nn.Module):
         else:
             if not spatial_self_attn:
                 context = context[:, :self.text_context_len, :]
+            if self._kv_fused:
+                k, v = self.to_kv(context).chunk(2, dim=-1)
+            else:
                 k = self.to_k(context)
                 v = self.to_v(context)
 

src/unifolm_wma/modules/networks/wma_model.py

@@ -690,6 +690,8 @@ class WMAModel(nn.Module):
         self._ctx_cache = {}
         # fs_embed cache
         self._fs_embed_cache = None
+        # Pre-created CUDA stream for parallel action/state UNet
+        self._side_stream = torch.cuda.Stream() if not self.base_model_gen_only else None
 
     def forward(self,
                 x: Tensor,
@@ -849,8 +851,8 @@ class WMAModel(nn.Module):
         if not self.base_model_gen_only:
             ba, _, _ = x_action.shape
             ts_state = timesteps[:ba] if b > 1 else timesteps
-            # Run action_unet and state_unet in parallel via CUDA streams
+            # Run action_unet and state_unet in parallel via pre-created CUDA stream
-            s_stream = torch.cuda.Stream()
+            s_stream = self._side_stream
             s_stream.wait_stream(torch.cuda.current_stream())
             with torch.cuda.stream(s_stream):
                 s_y = self.state_unet(x_state, ts_state, hs_a,

unitree_z1_dual_arm_cleanup_pencils/case1/output.log

@@ -1,14 +1,14 @@
 /mnt/ASC1637/miniconda3/envs/unifolm-wma-o/lib/python3.10/site-packages/lightning_fabric/__init__.py:29: UserWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools<81.
   __import__("pkg_resources").declare_namespace(__name__)
-2026-02-10 10:36:44.797852: I tensorflow/core/util/port.cc:113] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
+2026-02-11 06:58:19.745318: I tensorflow/core/util/port.cc:113] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
-2026-02-10 10:36:44.801300: I external/local_tsl/tsl/cuda/cudart_stub.cc:31] Could not find cuda drivers on your machine, GPU will not be used.
+2026-02-11 06:58:19.748691: I external/local_tsl/tsl/cuda/cudart_stub.cc:31] Could not find cuda drivers on your machine, GPU will not be used.
-2026-02-10 10:36:44.837891: E external/local_xla/xla/stream_executor/cuda/cuda_dnn.cc:9261] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
+2026-02-11 06:58:19.782405: E external/local_xla/xla/stream_executor/cuda/cuda_dnn.cc:9261] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
-2026-02-10 10:36:44.837946: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:607] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered
+2026-02-11 06:58:19.782465: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:607] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered
-2026-02-10 10:36:44.839880: E external/local_xla/xla/stream_executor/cuda/cuda_blas.cc:1515] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
+2026-02-11 06:58:19.784464: E external/local_xla/xla/stream_executor/cuda/cuda_blas.cc:1515] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
-2026-02-10 10:36:44.849073: I external/local_tsl/tsl/cuda/cudart_stub.cc:31] Could not find cuda drivers on your machine, GPU will not be used.
+2026-02-11 06:58:19.793381: I external/local_tsl/tsl/cuda/cudart_stub.cc:31] Could not find cuda drivers on your machine, GPU will not be used.
-2026-02-10 10:36:44.849365: I tensorflow/core/platform/cpu_feature_guard.cc:182] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.
+2026-02-11 06:58:19.794103: I tensorflow/core/platform/cpu_feature_guard.cc:182] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.
 To enable the following instructions: AVX2 AVX512F AVX512_VNNI AVX512_BF16 FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.
-2026-02-10 10:36:45.644793: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT
+2026-02-11 06:58:20.607029: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT
 [rank: 0] Global seed set to 123
 /mnt/ASC1637/miniconda3/envs/unifolm-wma-o/lib/python3.10/site-packages/kornia/feature/lightglue.py:44: FutureWarning: `torch.cuda.amp.custom_fwd(args...)` is deprecated. Please use `torch.amp.custom_fwd(args..., device_type='cuda')` instead.
   @torch.cuda.amp.custom_fwd(cast_inputs=torch.float32)
@@ -41,6 +41,7 @@ INFO:root:Loading pretrained ViT-H-14 weights (laion2b_s32b_b79k).
     ⚠ Found 601 fp32 params, converting to bf16
     ✓ All parameters converted to bfloat16
     ✓ torch.compile: 3 ResBlocks in output_blocks[5, 8, 9]
+    ✓ KV fused: 66 attention layers
 INFO:root:***** Configing Data *****
 >>> unitree_z1_stackbox: 1 data samples loaded.
 >>> unitree_z1_stackbox: data stats loaded.
@@ -65,8 +66,31 @@ DEBUG:PIL.PngImagePlugin:STREAM b'pHYs' 41 9
 DEBUG:PIL.PngImagePlugin:STREAM b'IDAT' 62 4096
 
   0%|          | 0/8 [00:00<?, ?it/s]/mnt/ASC1637/miniconda3/envs/unifolm-wma-o/lib/python3.10/site-packages/torch/nn/functional.py:5501: UserWarning: Attempting to use hipBLASLt on an unsupported architecture! Overriding blas backend to hipblas (Triggered internally at ../aten/src/ATen/Context.cpp:296.)
+  proj = linear(q, w, b)
 
  12%|█▎        | 1/8 [01:01<07:08, 61.25s/it]
+ 25%|██▌       | 2/8 [01:58<05:53, 58.90s/it]
+ 38%|███▊      | 3/8 [02:55<04:50, 58.14s/it]
+ 50%|█████     | 4/8 [03:52<03:51, 57.79s/it]
+ 62%|██████▎   | 5/8 [04:50<02:52, 57.60s/it]
+ 75%|███████▌  | 6/8 [05:47<01:54, 57.48s/it]
+ 88%|████████▊ | 7/8 [06:44<00:57, 57.41s/it]
+100%|██████████| 8/8 [07:42<00:00, 57.36s/it]
+100%|██████████| 8/8 [07:42<00:00, 57.75s/it]
+>>> Step 0: generating actions ...
+>>> Step 0: interacting with world model ...
+>>>>>>>>>>>>>>>>>>>>>>>>
+>>> Step 1: generating actions ...
+>>> Step 1: interacting with world model ...
+>>>>>>>>>>>>>>>>>>>>>>>>
+>>> Step 2: generating actions ...
+>>> Step 2: interacting with world model ...
+>>>>>>>>>>>>>>>>>>>>>>>>
+>>> Step 3: generating actions ...
+>>> Step 3: interacting with world model ...
+>>>>>>>>>>>>>>>>>>>>>>>>
+>>> Step 4: generating actions ...
+>>> Step 4: interacting with world model ...
 >>>>>>>>>>>>>>>>>>>>>>>>
 >>> Step 5: generating actions ...
 >>> Step 5: interacting with world model ...
@@ -116,30 +140,7 @@ DEBUG:PIL.Image:Importing WmfImagePlugin
 DEBUG:PIL.Image:Importing PpmImagePlugin
 DEBUG:PIL.Image:Importing PsdImagePlugin
 DEBUG:PIL.Image:Importing QoiImagePlugin
-DEBUG:PIL.Image:Importing XVThumbImagePlugin
-
- 12%|█▎        | 1/8 [01:06<07:46, 66.62s/it]
- 25%|██▌       | 2/8 [02:07<06:17, 62.97s/it]
- 38%|███▊      | 3/8 [03:07<05:08, 61.80s/it]
- 50%|█████     | 4/8 [04:07<04:05, 61.30s/it]
- 62%|██████▎   | 5/8 [05:08<03:03, 61.02s/it]
- 75%|███████▌  | 6/8 [06:08<02:01, 60.84s/it]
- 88%|████████▊ | 7/8 [07:09<01:00, 60.68s/it]
-100%|██████████| 8/8 [08:09<00:00, 60.66s/it]
-100%|██████████| 8/8 [08:09<00:00, 61.25s/it]
->>>>>>>>>>>>>>>>>>>>>>>>
->>> Step 1: generating actions ...
->>> Step 1: interacting with world model ...
->>>>>>>>>>>>>>>>>>>>>>>>
->>> Step 2: generating actions ...
->>> Step 2: interacting with world model ...
->>>>>>>>>>>>>>>>>>>>>>>>
->>> Step 3: generating actions ...
->>> Step 3: interacting with world model ...
->>>>>>>>>>>>>>>>>>>>>>>>
->>> Step 4: generating actions ...
->>> Step 4: interacting with world model ...
 DEBUG:PIL.Image:Importing SgiImagePlugin
->>> Step 5: generating actions ...
+DEBUG:PIL.Image:Importing SpiderImagePlugin
->>> Step 5: interacting with world model ...
+DEBUG:PIL.Image:Importing SunImagePlugin
->>>>>>>>>>>>>>>>>>>>>>>>
+DEBUG:PIL.Image:Importing TgaImagePlugin

unitree_z1_dual_arm_cleanup_pencils/case1/psnr_result1.json

@@ -1,5 +1,5 @@
 {
     "gt_video": "/mnt/ASC1637/unifolm-world-model-action/unitree_z1_dual_arm_cleanup_pencils/case1/output/inference/unitree_z1_dual_arm_cleanup_pencils_case1_amd.mp4",
     "pred_video": "/mnt/ASC1637/unifolm-world-model-action/unitree_z1_dual_arm_cleanup_pencils/case1/output/inference/0_full_fs4.mp4",
-    "psnr": 31.802224855380352
+    "psnr": 30.34518638635329
 }