Fix four logic bugs in Refiner V2 advisor and streak tracking
- Fix _v2_find_perfect_example_for_intent: was reading non-existent loved_delta_sources field; now correctly reads perfect_anchors/base and loved_variants using the actual positive_prompt/encoded_prompt keys - Fix _v2_build_v2_recommendations: removed global phrase_memory liked phrases that would inject unrelated-intent vocabulary for universal keys; recommendations now only include axis feedback and intent-filtered repair candidates - Fix _v2_update_streaks in Prompt only mode: avg_reward_ema and good/bad streaks are now skipped in Prompt only mode so they cannot contaminate conditioning adaptation strength in subsequent Refine runs; last_rating_label and last_missing_axes still update for repair continuity - Fix advisor rating label on reset/first run: advisor now receives "No previous output (first run or session reset)" instead of the user-set rating widget value when has_previous_run is False, preventing the advisor from treating a stale rating as evidence Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
This commit is contained in:
digital-garbage
co-authored by
Claude Sonnet 4.6
Claude Sonnet 4.6
parent
2701c10631
commit
4ed7f401a5
+34
-40
@@ -8226,20 +8226,21 @@ class FunPackVideoRefinerV2(FunPackVideoRefiner):
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global_state["bad_conditioning"] = payload
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global_state["bad_conditioning_count"] = int(global_state.get("bad_conditioning_count", 0)) + 1
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def _v2_update_streaks(self, global_state, rating_profile):
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def _v2_update_streaks(self, global_state, rating_profile, update_conditioning_strength=True):
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reward = float(rating_profile.get("reward", 0.0))
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avg = float(global_state.get("avg_reward_ema", 0.0))
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avg = 0.86 * avg + 0.14 * reward
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global_state["avg_reward_ema"] = round(avg, 6)
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if rating_profile.get("key") == "like":
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global_state["good_streak"] = int(global_state.get("good_streak", 0)) + 1
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global_state["bad_streak"] = 0
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elif reward < -0.25:
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global_state["bad_streak"] = int(global_state.get("bad_streak", 0)) + 1
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global_state["good_streak"] = 0
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else:
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global_state["good_streak"] = 0
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global_state["bad_streak"] = 0
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if update_conditioning_strength:
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avg = float(global_state.get("avg_reward_ema", 0.0))
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avg = 0.86 * avg + 0.14 * reward
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global_state["avg_reward_ema"] = round(avg, 6)
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if rating_profile.get("key") == "like":
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global_state["good_streak"] = int(global_state.get("good_streak", 0)) + 1
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global_state["bad_streak"] = 0
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elif reward < -0.25:
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global_state["bad_streak"] = int(global_state.get("bad_streak", 0)) + 1
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global_state["good_streak"] = 0
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else:
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global_state["good_streak"] = 0
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global_state["bad_streak"] = 0
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global_state["last_rating_label"] = rating_profile.get("label", "")
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global_state["last_missing_axes"] = list(rating_profile.get("missing_axes", []))
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@@ -8796,20 +8797,22 @@ class FunPackVideoRefinerV2(FunPackVideoRefiner):
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return serializable
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def _v2_find_perfect_example_for_intent(self, global_state, intent_family_slot):
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if isinstance(intent_family_slot, dict):
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for entry in intent_family_slot.get("loved_delta_sources", []):
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if isinstance(entry, dict):
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prompt = self._v2_prompt_key(entry.get("prompt", ""))
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if prompt and str(entry.get("rating_label", "")).lower() in {"perfect", "like"}:
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return prompt
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for entry in sorted(
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intent_family_slot.get("intent_preference_phrases", {}).values(),
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key=lambda e: float(e.get("score", 0.0)) if isinstance(e, dict) else 0.0,
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reverse=True,
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):
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if not isinstance(entry, dict):
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continue
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prompt = self._v2_prompt_key(entry.get("source_prompt", ""))
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if not isinstance(intent_family_slot, dict):
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return ""
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base = intent_family_slot.get("perfect_anchors", {}).get("base", {})
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if isinstance(base, dict):
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prompt = self._v2_prompt_key(base.get("positive_prompt", "") or base.get("encoded_prompt", ""))
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if prompt:
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return prompt
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loved = intent_family_slot.get("loved_variants", {})
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if isinstance(loved, dict) and loved:
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best = max(
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(e for e in loved.values() if isinstance(e, dict)),
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key=lambda e: int(e.get("last_seen_iter", 0)),
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default=None,
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)
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if best:
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prompt = self._v2_prompt_key(best.get("positive_prompt", "") or best.get("encoded_prompt", ""))
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if prompt:
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return prompt
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return ""
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@@ -8826,16 +8829,6 @@ class FunPackVideoRefinerV2(FunPackVideoRefiner):
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texts = [str(c.get("text", "")) for c in repair_candidates[:6] if isinstance(c, dict) and c.get("text")]
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if texts:
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parts.append(f"Repair suggestions from memory: {', '.join(texts)}.")
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phrase_mem = global_state.get("phrase_memory", {}) if isinstance(global_state, dict) else {}
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top_liked = sorted(
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(e for e in phrase_mem.values() if isinstance(e, dict) and int(e.get("liked_count", 0) or 0) >= 2),
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key=lambda e: float(e.get("score", 0.0)),
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reverse=True,
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)[:4]
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if top_liked:
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liked_texts = [str(e.get("text", "")) for e in top_liked if e.get("text")]
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if liked_texts:
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parts.append(f"Consistently liked phrases: {', '.join(liked_texts)}.")
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return " ".join(parts) if parts else ""
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def _v2_advisor_prompt(
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@@ -9939,7 +9932,7 @@ class FunPackVideoRefinerV2(FunPackVideoRefiner):
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memory_status = f"{memory_status}\n{scene_sync_status}\n{intent_family_status}\n{intent_learning_status}"
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self._v2_update_conditioning_memory(global_state, previous_run, learning_profile, axis_feedback)
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if has_previous_run and not learning_profile.get("skip_learning"):
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self._v2_update_streaks(global_state, learning_profile)
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self._v2_update_streaks(global_state, learning_profile, update_conditioning_strength=not prompt_only_mode)
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repair_feedback, repair_persistence_status = self._v2_active_repair_feedback(
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global_state,
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axis_feedback,
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@@ -10079,12 +10072,13 @@ class FunPackVideoRefinerV2(FunPackVideoRefiner):
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v2_recommendations = self._v2_build_v2_recommendations(repair_feedback, phrases, repair_candidates, global_state)
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perfect_example = self._v2_find_perfect_example_for_intent(global_state, current_family_slot)
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pre_advisor_prompt = prompt_to_encode
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advisor_rating_label = rating_label if has_previous_run else "No previous output (first run or session reset)"
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prompt_to_encode, advisor_status, advisor_diagnostic, advisor_applied = self._v2_prompt_advisor(
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advisor_clip,
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advisor_mode,
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prompt_to_encode,
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intent_source_prompt,
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rating_label,
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advisor_rating_label,
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repair_feedback,
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phrases,
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repair_candidates,
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@@ -10170,7 +10164,7 @@ class FunPackVideoRefinerV2(FunPackVideoRefiner):
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prompt_to_encode,
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repaired_negative_prompt,
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intent_source_prompt,
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rating_label,
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advisor_rating_label,
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learning_profile,
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repair_feedback,
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previous_run=previous_run,
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