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Add wrong-intent V2 ratings

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digital-garbage
2026-05-08 16:12:00 +03:00
parent 5d057a95d9
commit 8daf151b68
3 changed files with 87 additions and 2 deletions
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CHANGELOG.md
+2
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@@ -14,6 +14,8 @@ Improved `FunPack Video Refiner V2` training data output with clearer sections a
Updated `FunPack Video Refiner V2` to remember liked action/detail phrase clusters with their neighbors and use those ordered clusters before weaker ngram or token memory when repairing missing axes.
Added `Wrong details`, `Wrong action`, and `Wrong details + action` ratings for good-looking videos that do not match the requested intent; these preserve satisfied quality/composition signals while marking the mismatched action/detail context for repair.
## [2.2.0] - 2026-05-07
### Added
conditioning.py
+59 -2
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@@ -5474,6 +5474,9 @@ V2_RATING_LABELS = [
"Missing action",
"Missing quality",
"Missing details + action",
"Wrong details",
"Wrong action",
"Wrong details + action",
"Missing details + quality",
"Missing action + quality",
"Awful",
@@ -5487,6 +5490,9 @@ V2_RATING_PROFILES = {
"Missing action": {"key": "missing_action", "reward": 0.05, "level": 5, "missing_axes": ["action"]},
"Missing quality": {"key": "missing_quality", "reward": -0.30, "level": 4, "missing_axes": ["quality"]},
"Missing details + action": {"key": "missing_details_action", "reward": -0.10, "level": 3, "missing_axes": ["details", "action"]},
"Wrong details": {"key": "wrong_details", "reward": 0.20, "level": 5, "missing_axes": ["details"], "wrong_axes": ["details"]},
"Wrong action": {"key": "wrong_action", "reward": 0.10, "level": 4, "missing_axes": ["action"], "wrong_axes": ["action"]},
"Wrong details + action": {"key": "wrong_details_action", "reward": 0.00, "level": 3, "missing_axes": ["details", "action"], "wrong_axes": ["details", "action"]},
"Missing details + quality": {"key": "missing_details_quality", "reward": -0.40, "level": 2, "missing_axes": ["details", "quality"]},
"Missing action + quality": {"key": "missing_action_quality", "reward": -0.55, "level": 1, "missing_axes": ["action", "quality"]},
"Awful": {"key": "awful", "reward": -0.90, "level": 0, "missing_axes": ["details", "action", "quality"]},
@@ -5509,6 +5515,12 @@ V2_RATING_ALIASES = {
"Missing quality + concepts": "Missing action + quality",
"Missing quality + action": "Missing action + quality",
"Missing action + details": "Missing details + action",
"Wrong action + details": "Wrong details + action",
"Wrong intent": "Wrong details + action",
"Wrong concept": "Wrong action",
"Wrong concepts": "Wrong action",
"Wrong movement": "Wrong action",
"Wrong micro-movements": "Wrong details",
"Missing everything": "Awful",
}
@@ -6105,10 +6117,12 @@ class FunPackVideoRefinerV2(FunPackVideoRefiner):
"satisfied_count": 0,
"resolved_count": 0,
"regressed_count": 0,
"wrong_count": 0,
"bad_count": 0,
"wanted_axes": {},
"satisfied_axes": {},
"resolved_axes": {},
"wrong_axes": {},
"positions": {},
})
entry.setdefault("text", text)
@@ -6133,10 +6147,12 @@ class FunPackVideoRefinerV2(FunPackVideoRefiner):
entry.setdefault("satisfied_count", 0)
entry.setdefault("resolved_count", 0)
entry.setdefault("regressed_count", 0)
entry.setdefault("wrong_count", 0)
entry.setdefault("bad_count", 0)
entry.setdefault("wanted_axes", {})
entry.setdefault("satisfied_axes", {})
entry.setdefault("resolved_axes", {})
entry.setdefault("wrong_axes", {})
entry.setdefault("positions", {})
return entry
@@ -6339,14 +6355,17 @@ class FunPackVideoRefinerV2(FunPackVideoRefiner):
"satisfied_axes": [],
"resolved_axes": [],
"regressed_axes": [],
"wrong_axes": [],
}
all_axes = set(V2_FEEDBACK_AXES)
missing_axes = set(rating_profile.get("missing_axes", [])) & all_axes
wrong_axes = set(rating_profile.get("wrong_axes", [])) & all_axes
if key == "awful":
missing_axes = set(all_axes)
elif key == "like":
missing_axes = set()
wrong_axes = set()
has_previous_axis_signal = previous_missing_axes is not None
previous_missing = set(previous_missing_axes or []) & all_axes
@@ -6359,6 +6378,7 @@ class FunPackVideoRefinerV2(FunPackVideoRefiner):
"satisfied_axes": self._v2_order_axes(satisfied_axes),
"resolved_axes": self._v2_order_axes(resolved_axes),
"regressed_axes": self._v2_order_axes(regressed_axes),
"wrong_axes": self._v2_order_axes(wrong_axes),
}
def _v2_axis_feedback_status(self, axis_feedback):
@@ -6373,7 +6393,8 @@ class FunPackVideoRefinerV2(FunPackVideoRefiner):
f"Axis feedback: missing {fmt('missing_axes')} | "
f"satisfied {fmt('satisfied_axes')} | "
f"resolved {fmt('resolved_axes')} | "
f"regressed {fmt('regressed_axes')}."
f"regressed {fmt('regressed_axes')} | "
f"wrong {fmt('wrong_axes')}."
)
def _v2_top_category_summary(self, scores, limit=3):
@@ -6398,6 +6419,12 @@ class FunPackVideoRefinerV2(FunPackVideoRefiner):
"Guidance: '-Just forget it-' skipped learning. Use it for workflow/model/seed failures; "
"use a missing-axis rating when the prompt should teach the node."
)
if learning_profile.get("wrong_axes"):
return (
"Guidance: wrong-intent rating preserved satisfied axes but marked the current "
"action/detail context as wrong for this request. The next run will repair from intent "
"and preferred context clusters."
)
missing_axes = set(axis_feedback.get("missing_axes", [])) if isinstance(axis_feedback, dict) else set()
resolved_axes = set(axis_feedback.get("resolved_axes", [])) if isinstance(axis_feedback, dict) else set()
@@ -6586,6 +6613,7 @@ class FunPackVideoRefinerV2(FunPackVideoRefiner):
satisfied_axes = set(axis_feedback.get("satisfied_axes", []))
resolved_axes = set(axis_feedback.get("resolved_axes", []))
regressed_axes = set(axis_feedback.get("regressed_axes", []))
wrong_axes = set(axis_feedback.get("wrong_axes", []))
reward = float(rating_profile.get("reward", 0.0))
preferred_context_status = self._v2_update_preferred_context_memory(
global_state,
@@ -6633,6 +6661,7 @@ class FunPackVideoRefinerV2(FunPackVideoRefiner):
matched_satisfied = phrase_axes & satisfied_axes
matched_resolved = phrase_axes & resolved_axes
matched_regressed = phrase_axes & regressed_axes
matched_wrong = phrase_axes & wrong_axes
rating_evidence = entry.setdefault("rating_evidence", {})
weights_before = dict(weights)
@@ -6669,6 +6698,32 @@ class FunPackVideoRefinerV2(FunPackVideoRefiner):
bump_evidence("missing_axes", missing_axes)
for axis in self._v2_order_axes(missing_axes):
train_axis(axis, 0.045 * kind_scale, prefer_current=False)
elif wrong_axes:
if matched_wrong:
delta = -0.18 * kind_scale
entry["wrong_count"] = int(entry.get("wrong_count", 0)) + 1
wrong_axis_counts = entry.setdefault("wrong_axes", {})
for axis in self._v2_order_axes(matched_wrong):
wrong_axis_counts[axis] = int(wrong_axis_counts.get(axis, 0)) + 1
train_axis(axis, 0.12 * kind_scale, prefer_current=False)
primary_before, _ = self._v2_scores_primary(effective_before)
weights[primary_before] = float(weights.get(primary_before, 0.0)) - 0.055 * kind_scale
elif matched_satisfied:
delta = 0.18 * kind_scale
entry["satisfied_count"] = int(entry.get("satisfied_count", 0)) + 1
for axis in self._v2_order_axes(matched_satisfied):
train_axis(axis, 0.070 * kind_scale)
else:
delta = 0.02 * kind_scale
wanted_axes = entry.setdefault("wanted_axes", {})
for axis in self._v2_order_axes(wrong_axes):
wanted_axes[axis] = int(wanted_axes.get(axis, 0)) + 1
satisfied_axis_counts = entry.setdefault("satisfied_axes", {})
for axis in self._v2_order_axes(matched_satisfied):
satisfied_axis_counts[axis] = int(satisfied_axis_counts.get(axis, 0)) + 1
bump_evidence("wrong_axes", wrong_axes)
bump_evidence("satisfied_axes", satisfied_axes)
elif missing_axes:
for axis in self._v2_order_axes(missing_axes - matched_missing):
train_axis(axis, 0.14 * kind_scale, prefer_current=False)
@@ -7096,7 +7151,8 @@ class FunPackVideoRefinerV2(FunPackVideoRefiner):
min(1.2, axis_hits * 0.34) +
min(0.5, evidence * 0.05) +
min(0.4, int(entry.get("liked_count", 0) or 0) * 0.08) -
min(1.0, int(entry.get("bad_count", 0) or 0) * 0.20)
min(1.0, int(entry.get("bad_count", 0) or 0) * 0.20) -
min(0.9, int(entry.get("wrong_count", 0) or 0) * 0.16)
)
if axes & missing_axes and score > 0.60:
add_candidate(entry.get("text", text), axes, score, "memory")
@@ -7186,6 +7242,7 @@ class FunPackVideoRefinerV2(FunPackVideoRefiner):
score += min(1.0, int(entry.get("liked_count", 0)) * 0.14)
score += min(1.2, sum(int(v) for v in entry.get("wanted_axes", {}).values()) * 0.10)
score -= min(1.6, int(entry.get("bad_count", 0)) * 0.22)
score -= min(1.1, int(entry.get("wrong_count", 0)) * 0.18)
if score <= 0.10:
continue
scored.append((score, entry.get("primary", "details"), str(entry.get("text", text)).strip()))
tests/test_v2_category_alignment.py
+26
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@@ -577,3 +577,29 @@ def test_repair_candidates_saved_as_json_lists(tmp_path):
assert candidates
assert isinstance(candidates[0]["axes"], list)
assert "walking" in state["last_run"]["encoded_prompt"]
def test_wrong_action_rating_preserves_quality_but_marks_action_wrong():
refiner = FunPackVideoRefinerV2()
global_state = {"phrase_memory": {}, "preferred_context_memory": {}}
phrases = refiner._v2_classify_phrases(
None,
prompt_items(refiner, ["walking", "cinematic lighting"]),
global_state,
)
profile = normalize_refiner_v2_rating("Wrong action")
feedback = refiner._v2_axis_feedback(profile, None)
refiner._v2_update_phrase_memory(
global_state,
{"prompt": "walking cinematic lighting", "phrases": phrases},
profile,
1,
feedback,
)
assert feedback["missing_axes"] == ["action"]
assert feedback["wrong_axes"] == ["action"]
assert feedback["satisfied_axes"] == ["details", "quality"]
assert global_state["phrase_memory"]["walking"]["wrong_count"] == 1
assert global_state["phrase_memory"]["cinematic lighting"]["satisfied_count"] == 1