feat: per-guide attention strength control in self-attention (#12518)

Implements per-guide attention attenuation via log-space additive bias
in self-attention. Each guide reference tracks its own strength and
optional spatial mask in conditioning metadata (guide_attention_entries).

comfy_extras/nodes_lt.py

@@ -134,6 +134,36 @@ class LTXVImgToVideoInplace(io.ComfyNode):
     generate = execute  # TODO: remove
 
 
+def _append_guide_attention_entry(positive, negative, pre_filter_count, latent_shape, strength=1.0):
+    """Append a guide_attention_entry to both positive and negative conditioning.
+
+    Each entry tracks one guide reference for per-reference attention control.
+    Entries are derived independently from each conditioning to avoid cross-contamination.
+    """
+    new_entry = {
+        "pre_filter_count": pre_filter_count,
+        "strength": strength,
+        "pixel_mask": None,
+        "latent_shape": latent_shape,
+    }
+    results = []
+    for cond in (positive, negative):
+        # Read existing entries from this specific conditioning
+        existing = []
+        for t in cond:
+            found = t[1].get("guide_attention_entries", None)
+            if found is not None:
+                existing = found
+                break
+        # Shallow copy and append (no deepcopy needed — entries contain
+        # only scalars and None for pixel_mask at this call site).
+        entries = [*existing, new_entry]
+        results.append(node_helpers.conditioning_set_values(
+            cond, {"guide_attention_entries": entries}
+        ))
+    return results[0], results[1]
+
+
 def conditioning_get_any_value(conditioning, key, default=None):
     for t in conditioning:
         if key in t[1]:
@@ -324,6 +354,13 @@ class LTXVAddGuide(io.ComfyNode):
             scale_factors,
         )
 
+        # Track this guide for per-reference attention control.
+        pre_filter_count = t.shape[2] * t.shape[3] * t.shape[4]
+        guide_latent_shape = list(t.shape[2:])  # [F, H, W]
+        positive, negative = _append_guide_attention_entry(
+            positive, negative, pre_filter_count, guide_latent_shape, strength=strength,
+        )
+
         return io.NodeOutput(positive, negative, {"samples": latent_image, "noise_mask": noise_mask})
 
     generate = execute  # TODO: remove
@@ -359,8 +396,14 @@ class LTXVCropGuides(io.ComfyNode):
         latent_image = latent_image[:, :, :-num_keyframes]
         noise_mask = noise_mask[:, :, :-num_keyframes]
 
-        positive = node_helpers.conditioning_set_values(positive, {"keyframe_idxs": None})
-        negative = node_helpers.conditioning_set_values(negative, {"keyframe_idxs": None})
+        positive = node_helpers.conditioning_set_values(positive, {
+            "keyframe_idxs": None,
+            "guide_attention_entries": None,
+        })
+        negative = node_helpers.conditioning_set_values(negative, {
+            "keyframe_idxs": None,
+            "guide_attention_entries": None,
+        })
 
         return io.NodeOutput(positive, negative, {"samples": latent_image, "noise_mask": noise_mask})