From f87432bafbeae8516c6b5b4e11c1d54a8a1da1fc Mon Sep 17 00:00:00 2001
From: kijai <40791699+kijai@users.noreply.github.com>
Date: Tue, 2 Jun 2026 00:17:43 +0300
Subject: [PATCH] Update nodes_bernini.py

---
 comfy_extras/nodes_bernini.py | 28 ++++++++++++++++++++--------
 1 file changed, 20 insertions(+), 8 deletions(-)

diff --git a/comfy_extras/nodes_bernini.py b/comfy_extras/nodes_bernini.py
index aab4dbd1b..c29af857e 100644
--- a/comfy_extras/nodes_bernini.py
+++ b/comfy_extras/nodes_bernini.py
@@ -24,10 +24,17 @@ class BerniniConditioning(io.ComfyNode):
     which the Wan model appends as extra tokens with per-stream source_id rope.
 
     The task is inferred from which inputs are connected:
-      (nothing)                 -> t2v
-      source_video              -> v2v
-      source_video + ref images -> rv2v
-      ref images only           -> r2v   (each kept at native aspect)
+      (nothing)                  -> t2v
+      source_video               -> v2v
+      source_video + ref images  -> rv2v
+      ref images only            -> r2v   (each kept at native aspect)
+      source_video + ref_video   -> video insertion / "ads2v"
+
+    source_video is the edit base / canvas (resized to width x height).
+    reference_video is moving content to composite in (e.g. a clip to play on a
+    screen), kept at its native aspect like the reference images. Streams are
+    ordered source_video, reference_video, then reference_images -> source_id
+    1, 2, 3... matching the reference repo's [base, content, refs].
     """
 
     @classmethod
@@ -46,7 +53,8 @@ class BerniniConditioning(io.ComfyNode):
                 io.Int.Input("height", default=480, min=16, max=8192, step=16),
                 io.Int.Input("length", default=81, min=1, max=8192, step=4),
                 io.Int.Input("batch_size", default=1, min=1, max=4096),
-                io.Image.Input("source_video", optional=True, tooltip="Source video to edit/restyle (original task v2v or rv2v). Resized to width/height and trimmed to length."),
+                io.Image.Input("source_video", optional=True, tooltip="Source video to edit/restyle (task v2v or rv2v). Resized to width/height and trimmed to length. Acts as the edit base / canvas."),
+                io.Image.Input("reference_video", optional=True, tooltip="Moving content to composite into the source video (video insertion / ads2v), e.g. a clip to play on a screen. Kept at native aspect (long edge capped at ref_max_size), trimmed to length."),
                 io.Image.Input("reference_images", optional=True, tooltip="Reference image(s) injected as in-context tokens (task r2v or rv2v). Each is kept at its native aspect ratio, long edge capped at ref_max_size."),
                 io.Int.Input("ref_max_size", default=848, min=16, max=8192, step=16, optional=True),
             ],
@@ -59,17 +67,21 @@ class BerniniConditioning(io.ComfyNode):
 
     @classmethod
     def execute(cls, positive, negative, vae, width, height, length, batch_size,
-                source_video=None, reference_images=None, ref_max_size=848) -> io.NodeOutput:
+                source_video=None, reference_video=None, reference_images=None, ref_max_size=848) -> io.NodeOutput:
         latent = torch.zeros([batch_size, 16, ((length - 1) // 4) + 1, height // 8, width // 8],
                              device=comfy.model_management.intermediate_device())
 
-        # Ordered list of condition streams: source video (source_id 1) first,
-        # then each reference image (source_id 2, 3, ...), the model assigns the source_id from list order.
+        # Ordered list of condition streams -> source_id by list order:
+        #   source_video (1), reference_video (2), reference_images (3, 4, ...).
         context = []
         if source_video is not None:
             vid = comfy.utils.common_upscale(source_video[:length, :, :, :3].movedim(-1, 1), width, height, "area", "center").movedim(1, -1)
             context.append(vae.encode(vid[:, :, :, :3]))
 
+        if reference_video is not None:
+            ref_vid = _resize_long_edge(reference_video[:length], ref_max_size)  # moving content, native aspect
+            context.append(vae.encode(ref_vid[:, :, :, :3]))
+
         if reference_images is not None:
             for i in range(reference_images.shape[0]):
                 img = _resize_long_edge(reference_images[i:i + 1], ref_max_size)  # native aspect per ref