Batch: output ALL variant videos to the Chain Sampler IMAGES port

Previously the batch path returned only the last variant's frames on the
IMAGES slot. Collect each variant's decoded frames (CPU) and concatenate
them so the whole batch comes out IMAGES back-to-back — enabling the
"just make N slight variations, don't rate them" use case. Falls back to
the last variant if frame shapes are incompatible. (Phase 1 of Interactive
Guessing.)

Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>

samplers.py

@@ -2905,6 +2905,7 @@ class FunPackLTXAVSceneChainSampler:
         entries = list(enumerate(variants))   # [(idx, variant_positive_list), ...]
         manifest["iterations"] = len(entries)
         last = None
+        batch_images = []  # collect each variant's decoded frames for the IMAGES output
         base_seed = int(seed)
         for idx, pos_i in entries:
             iter_seed = base_seed + idx
@@ -2920,6 +2921,10 @@ class FunPackLTXAVSceneChainSampler:
                 unique_id=None, prompt=None,
             )
             last = out
+            # Collect this variant's decoded frames (CPU) so the IMAGES output shows ALL
+            # videos back-to-back, not just the last one. The sub-call already decoded them.
+            if isinstance(out[1], torch.Tensor) and out[1].dim() == 4 and out[1].shape[1] > 8:
+                batch_images.append(out[1].detach().cpu())
             out_latent = out[0]
             iid = f"{safe}_{stamp}_{idx:02d}"
             latent_path = os.path.join(batch_dir, f"{iid}.latent.pt")
@@ -2963,4 +2968,17 @@ class FunPackLTXAVSceneChainSampler:
         print(f"[FunPackSceneChain] {status}")
         if last is None:
             raise RuntimeError("Batch Training produced no output.")
-        return (last[0], last[1], status, last[3], last[4], last[5])
+        # IMAGES output = all variants' frames concatenated (so you can view the whole batch
+        # without rating). Falls back to the last variant if shapes are incompatible.
+        all_images = last[1]
+        compatible = [im for im in batch_images if im.shape[1:] == batch_images[0].shape[1:]] if batch_images else []
+        if len(compatible) > 1:
+            try:
+                all_images = torch.cat(compatible, dim=0)
+                print(f"[FunPackSceneChain] Batch IMAGES: {len(compatible)} videos concatenated "
+                      f"({all_images.shape[0]} frames total).")
+            except Exception as e:
+                print(f"[FunPackSceneChain] Batch IMAGES concat failed ({e}); returning last only.")
+        elif compatible:
+            all_images = compatible[0]
+        return (last[0], all_images, status, last[3], last[4], last[5])