Batch Training: remove debug diagnostics; bump version to 2.7.7

Batch Training confirmed working end to end (controlled batch with transitions, distinct
per-variant seeds, node rating ignored during a batch, per-entry rating + VF/axis learning
on submit). Stripped the temporary trace prints (Batch Training check / Batch check / ACTUAL
scene seeds), keeping clean progress logs. Gitignore the local .testvenv.

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

.gitignore

@@ -0,0 +1 @@
+.testvenv/

conditioning.py

@@ -12173,9 +12173,6 @@ class FunPackVideoRefinerV2(FunPackVideoRefiner):
         # conditioning just before each return below — so it wraps whatever output_conditioning
         # ends up being: single-scene OR multi-scene (transition split). Works WITH transitions.
         _do_batch = int(batch_variants or 1) > 1 and not learning_mode and not prompt_only_mode
-        print(f"[FunPackVideoRefinerV2] Batch Training check: batch_variants={batch_variants}, "
-              f"split_by_transitions={split_by_transitions}, learning_mode={learning_mode}, "
-              f"prompt_only={prompt_only_mode}, will_batch={_do_batch}")
         if split_by_transitions:
             try:
                 scene_texts = split_scene_texts
@@ -12295,8 +12292,7 @@ class FunPackVideoRefinerV2(FunPackVideoRefiner):
             m["funpack_scene_seed"] = (variant_seed[vi] + scene_idx[vi]) & 0xffffffffffffffff
             scene_idx[vi] += 1
         print(f"[FunPackVideoRefinerV2] Batch Training: packed {n} variant(s) over "
-              f"{len(output_conditioning)} scene(s) = {len(packed)} entries; per-variant seeds = "
-              f"{[variant_seed[k] for k in sorted(variant_seed)]}")
+              f"{len(output_conditioning)} scene(s) = {len(packed)} entries.")
         return packed
 
     def _v2_build_batch_variants(self, raw_prompt, n, seed, clip, encode_cache, source_image,

pyproject.toml

@@ -2,7 +2,7 @@
 [project]
 name = "ComfyUI-FunPack" # Unique identifier for your node. Immutable after creation.
 description = "A set of nodes for experiments with video generation models, including prompt refinement, adaptive LoRA weight workflows, story tools, video stitching, lorebook enhancement and more."
-version = "2.7.6" # Custom Node version. Must be semantically versioned.
+version = "2.7.7" # Custom Node version. Must be semantically versioned.
 license = { text = "GNU GPL v3" }
 dependencies  = [] # Filled in from requirements.txt
 

samplers.py

@@ -2245,16 +2245,7 @@ class FunPackLTXAVSceneChainSampler:
         # Batch Training: Studio (the hub) packs N conditionings into positive, each scene entry
         # tagged 'funpack_batch_variant'. That marker is the only trigger — the sampler has no
         # batch-count input. Sample one chain per packed entry, persist each for rating in Studio.
-        try:
-            _marks = [(e[1].get("funpack_batch_variant")
-                       if isinstance(e, (list, tuple)) and len(e) > 1 and isinstance(e[1], dict) else None)
-                      for e in (positive or [])]
-        except Exception:
-            _marks = "?"
-        _vg = self._split_batch_variants(positive)
-        print(f"[FunPackSceneChain] Batch check: {len(positive or [])} positive entr(ies), "
-              f"variant markers={_marks}, variant groups={len(_vg) if _vg else 0}")
-        if _vg is not None:
+        if self._split_batch_variants(positive) is not None:
             return self._run_batch_training(
                 model, vae, positive, negative, sampler, sigmas, seed, latent_template,
                 num_frames_per_scene, frame_overlap, cfg, max_scenes, use_same_seed,
@@ -2521,9 +2512,7 @@ class FunPackLTXAVSceneChainSampler:
         base_seed = int(seed)
         for idx, pos_i in entries:
             iter_seed = base_seed + idx
-            _scene_seeds = [self._scene_seed(sc) for sc in (pos_i or [])]
-            print(f"[FunPackSceneChain] Batch Training {idx + 1}/{len(entries)} "
-                  f"(loop_seed={iter_seed}, ACTUAL noise scene_seeds={_scene_seeds})")
+            print(f"[FunPackSceneChain] Batch Training {idx + 1}/{len(entries)}")
             out = self.sample(
                 model, vae, pos_i, negative, sampler, sigmas, iter_seed, latent_template,
                 num_frames_per_scene, frame_overlap, cfg, max_scenes, use_same_seed=use_same_seed,