fix: handle zero-token KV cache expansion

src/scope/core/pipelines/longlive/blocks/recache_frames.py

@@ -165,6 +165,13 @@ def __call__(self, components, state: PipelineState) -> PipelineState:
         num_recache_frames = min(
             block_state.current_start_frame, components.config.local_attn_size
         )
+
+        # Guard: skip recaching when there are zero frames to avoid
+        # KV cache tensor dimension mismatches (e.g. expand size 0 vs existing cache size)
+        if num_recache_frames == 0:
+            self.set_block_state(state, block_state)
+            return components, state
+
         recache_start = block_state.current_start_frame - num_recache_frames
         recache_frames = (
             block_state.recache_buffer[:, -num_recache_frames:]

src/scope/core/pipelines/longlive/modules/causal_model.py

@@ -43,6 +43,11 @@ def causal_rope_apply(x, grid_sizes, freqs, start_frame=0):
     for i, (f, h, w) in enumerate(grid_sizes.tolist()):
         seq_len = f * h * w
 
+        # Guard: if any grid dimension is zero, skip rope and pass through
+        if seq_len == 0:
+            output.append(x[i])
+            continue
+
         # precompute multipliers
         x_i = torch.view_as_complex(
             x[i, :seq_len].to(torch.float64).reshape(seq_len, n, -1, 2)

src/scope/core/pipelines/wan2_1/blocks/clean_kv_cache.py

@@ -107,6 +107,12 @@ def __call__(self, components, state: PipelineState) -> tuple[Any, PipelineState
         generator_param = next(components.generator.parameters())
 
         _, num_frames, _, _, _ = block_state.latents.shape
+
+        # Guard: skip cache cleaning when there are zero frames to avoid
+        # KV cache tensor dimension mismatches (e.g. expand size 0 vs existing cache size)
+        if num_frames == 0:
+            self.set_block_state(state, block_state)
+            return components, state
         current_end_frame = block_state.current_start_frame + num_frames
 
         # This is defined to give us timestep = 0 while matching shape expected by the generator.

src/scope/core/pipelines/wan2_1/blocks/denoise.py

@@ -150,6 +150,14 @@ def __call__(self, components, state: PipelineState) -> tuple[Any, PipelineState
         noise = block_state.latents
         batch_size = noise.shape[0]
         num_frames = noise.shape[1]
+
+        # Guard: skip denoising when there are zero frames to avoid
+        # KV cache tensor dimension mismatches (e.g. expand size 0 vs existing cache size)
+        if num_frames == 0:
+            block_state.latents = noise
+            self.set_block_state(state, block_state)
+            return components, state
+
         denoising_step_list = block_state.current_denoising_step_list.clone()
 
         conditional_dict = {"prompt_embeds": block_state.conditioning_embeds}