From 6a53695006e7bfe1f1823ab38d720e8356dd3726 Mon Sep 17 00:00:00 2001
From: ozbayb <17261091+ozbayb@users.noreply.github.com>
Date: Sun, 12 Apr 2026 20:18:10 -0600
Subject: [PATCH] LTX2 context windows - Skip guide frames in freenoise shuffle

---
 comfy/context_windows.py | 40 ++++++++++++++++++++++++----------------
 1 file changed, 24 insertions(+), 16 deletions(-)

diff --git a/comfy/context_windows.py b/comfy/context_windows.py
index 409bcc271..8fb7b9642 100644
--- a/comfy/context_windows.py
+++ b/comfy/context_windows.py
@@ -340,13 +340,31 @@ class IndexListContextHandler(ContextHandlerABC):
                     return model_conds['latent_shapes'].cond
         return None
 
+    @staticmethod
+    def _get_guide_entries(conds):
+        for cond_list in conds:
+            if cond_list is None:
+                continue
+            for cond_dict in cond_list:
+                model_conds = cond_dict.get('model_conds', {})
+                entries = model_conds.get('guide_attention_entries')
+                if entries is not None and hasattr(entries, 'cond') and entries.cond:
+                    return entries.cond
+        return None
+
     def _apply_freenoise(self, noise: torch.Tensor, conds: list[list[dict]], seed: int) -> torch.Tensor:
-        """Apply FreeNoise shuffling, scaling context length/overlap per-modality by frame ratio."""
+        """Apply FreeNoise shuffling, scaling context length/overlap per-modality by frame ratio.
+        If guide frames are present on the primary modality, only the video portion is shuffled.
+        """
+        guide_entries = self._get_guide_entries(conds)
+        guide_count = sum(e["latent_shape"][0] for e in guide_entries) if guide_entries else 0
+
         latent_shapes = self._get_latent_shapes(conds)
         if latent_shapes is not None and len(latent_shapes) > 1:
             modalities = comfy.utils.unpack_latents(noise, latent_shapes)
             primary_total = latent_shapes[0][self.dim]
-            modalities[0] = apply_freenoise(modalities[0], self.dim, self.context_length, self.context_overlap, seed)
+            primary_video_count = modalities[0].size(self.dim) - guide_count
+            apply_freenoise(modalities[0].narrow(self.dim, 0, primary_video_count), self.dim, self.context_length, self.context_overlap, seed)
             for i in range(1, len(modalities)):
                 mod_total = latent_shapes[i][self.dim]
                 ratio = mod_total / primary_total if primary_total > 0 else 1
@@ -355,7 +373,9 @@ class IndexListContextHandler(ContextHandlerABC):
                 modalities[i] = apply_freenoise(modalities[i], self.dim, mod_ctx_len, mod_ctx_overlap, seed)
             noise, _ = comfy.utils.pack_latents(modalities)
             return noise
-        return apply_freenoise(noise, self.dim, self.context_length, self.context_overlap, seed)
+        video_count = noise.size(self.dim) - guide_count
+        apply_freenoise(noise.narrow(self.dim, 0, video_count), self.dim, self.context_length, self.context_overlap, seed)
+        return noise
 
     def _build_window_state(self, x_in: torch.Tensor, conds: list[list[dict]]) -> WindowingState:
         """Build windowing state for the current step, including unpacking latents and extracting guide frame info from conds."""
@@ -367,19 +387,7 @@ class IndexListContextHandler(ContextHandlerABC):
         guide_latents_list = [None] * len(unpacked_latents)
         guide_entries_list = [None] * len(unpacked_latents)
 
-        # Scan for 'guide_attention_entries' in conds
-        extracted_guide_entries = None
-        for cond_list in conds:
-            if cond_list is None:
-                continue
-            for cond_dict in cond_list:
-                model_conds = cond_dict.get('model_conds', {})
-                entries = model_conds.get('guide_attention_entries')
-                if entries is not None and hasattr(entries, 'cond') and entries.cond:
-                    extracted_guide_entries = entries.cond
-                    break
-            if extracted_guide_entries is not None:
-                break
+        extracted_guide_entries = self._get_guide_entries(conds)
 
         # Strip guide frames (only from first modality for now)
         if extracted_guide_entries is not None: