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Add concept category feedback questions

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digital-garbage
2026-04-24 02:56:24 +03:00
parent 009e1edf8a
commit 95daea832c
2 changed files with 87 additions and 7 deletions
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conditioning.py
+85 -5
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@@ -92,6 +92,15 @@ RATING_PROFILES = {
},
}
CATEGORY_FEEDBACK_MAP = {
1: "general",
2: "concept",
3: "style",
4: "quality",
5: "character",
6: "details",
}
def _clamp(value, low, high):
return max(low, min(high, value))
@@ -772,6 +781,8 @@ class FunPackVideoRefiner:
"label": " ".join((phrase_words or [])[:6]),
"anchor_words": list(phrase_words or []),
"category": category,
"category_source": "auto",
"category_confidence": 0.35 if category == "general" else 0.65,
"word_importance": {},
"presence_target": 1.0,
"priority_weight": 1.0,
@@ -793,8 +804,13 @@ class FunPackVideoRefiner:
anchor_words = list(cluster.get("anchor_words", []))
defaults = self._default_concept_cluster(anchor_words)
defaults.update(cluster)
if not defaults.get("category") or defaults.get("category") == "general":
defaults.setdefault("category_source", "auto")
defaults.setdefault("category_confidence", 0.35 if defaults.get("category") == "general" else 0.65)
if defaults.get("category_source") != "user" and (
not defaults.get("category") or defaults.get("category") == "general"
):
defaults["category"] = self._infer_concept_category(defaults.get("anchor_words", []))
defaults["category_confidence"] = 0.35 if defaults["category"] == "general" else max(0.65, defaults.get("category_confidence", 0.0))
defaults["question_history"] = list(defaults.get("question_history", []))[-24:]
return defaults
@@ -827,6 +843,10 @@ class FunPackVideoRefiner:
"prompt": "What is your preference for having '{label}' in future outputs?",
"legend": "1=strongly less 2=less 3=slightly less 4=slightly more 5=more 6=much more",
},
"category": {
"prompt": "What kind of concept is '{label}'?",
"legend": "1=general 2=concept 3=style 4=quality 5=character 6=details",
},
}
def _get_concept_mean_importance(self, cluster: dict):
@@ -869,6 +889,10 @@ class FunPackVideoRefiner:
score = 0.38 * (2.0 - cluster.get("semantic_fidelity", 1.0)) + 0.24 * rating_shift + 0.20 * uncertainty + 0.18 * max(0.0, 1.0 - similarity)
elif question_type == "stability":
score = 0.35 * rating_shift + 0.30 * abs(cluster.get("stability_weight", 1.0) - 1.0) + 0.20 * max(0.0, 1.0 - similarity) + 0.15 * uncertainty
elif question_type == "category":
category_unknown = 1.0 if cluster.get("category_source") != "user" else 0.0
category_uncertainty = max(0.0, 1.0 - float(cluster.get("category_confidence", 0.35)))
score = 0.65 * category_unknown + 0.25 * category_uncertainty + 0.10 * rating_shift
else: # preference
score = 0.34 * abs(cluster.get("user_affinity", 1.0) - 1.0) + 0.28 * rating_shift + 0.20 * uncertainty + 0.18 * abs(cluster.get("priority_weight", 1.0) - 1.0)
@@ -884,6 +908,7 @@ class FunPackVideoRefiner:
question_specs = self._feedback_question_specs()
rating_shift = min(1.0, abs(rating - last_rating) / 4.0)
candidates = []
category_candidates = []
for cid in ordered_concept_ids:
cluster = concept_clusters.get(cid)
@@ -912,7 +937,7 @@ class FunPackVideoRefiner:
question_type, cluster, category, mean_imp,
neighbor_mean, rating_shift, similarity, iter_num
)
candidates.append({
candidate = {
"concept_id": cid,
"concept_label": current_concept_labels.get(cid, cluster.get("label", "")),
"question_type": question_type,
@@ -920,7 +945,16 @@ class FunPackVideoRefiner:
"group_id": chosen_group_id,
"category": category,
"score": score,
})
}
if question_type == "category":
category_candidates.append(candidate)
else:
candidates.append(candidate)
if category_candidates:
category_candidate = max(category_candidates, key=lambda x: x["score"])
if category_candidate["score"] >= 0.60:
return category_candidate
if not candidates:
return None
@@ -1072,6 +1106,37 @@ class FunPackVideoRefiner:
new_id = md5("|".join(sorted(phrase_words)).encode()).hexdigest()[:10]
return new_id, True
def _apply_category_feedback(self, cluster: dict, feedback_rating: int, iter_num: int = 0):
category = CATEGORY_FEEDBACK_MAP.get(int(feedback_rating), "general")
cluster["category"] = category
cluster["category_source"] = "user"
cluster["category_confidence"] = 1.0
if category in {"concept", "character", "details"}:
cluster["presence_target"] = self._clip_profile_value(cluster.get("presence_target", 1.0) + 0.10)
cluster["priority_weight"] = self._clip_profile_value(cluster.get("priority_weight", 1.0) + 0.10)
cluster["semantic_fidelity"] = self._clip_profile_value(
cluster.get("semantic_fidelity", 1.0) + 0.08,
low=0.6,
high=1.8,
)
elif category == "quality":
cluster["priority_weight"] = self._clip_profile_value(cluster.get("priority_weight", 1.0) + 0.06)
cluster["overrep_sensitivity"] = self._clip_profile_value(cluster.get("overrep_sensitivity", 1.0) + 0.08)
elif category == "style":
cluster["stability_weight"] = self._clip_profile_value(cluster.get("stability_weight", 1.0) + 0.08)
cluster["user_affinity"] = self._clip_profile_value(cluster.get("user_affinity", 1.0) + 0.05)
cluster["question_history"] = list(cluster.get("question_history", []))[-23:]
cluster["question_history"].append({
"iteration": iter_num,
"type": "category",
"rating": feedback_rating,
"category": category,
})
cluster["last_question_type"] = "category"
cluster["last_question_iter"] = iter_num
def _build_word_concept_map(self, prompt: str, concept_clusters: dict):
"""
Parse the prompt into concept phrases (Level 3), match or create clusters,
@@ -1099,10 +1164,16 @@ class FunPackVideoRefiner:
for w in phrase_words:
if w not in existing:
concept_clusters[cid]["anchor_words"].append(w)
if concept_clusters[cid].get("category") == "general":
if (
concept_clusters[cid].get("category_source") != "user" and
concept_clusters[cid].get("category") == "general"
):
concept_clusters[cid]["category"] = self._infer_concept_category(
concept_clusters[cid]["anchor_words"]
)
concept_clusters[cid]["category_confidence"] = (
0.35 if concept_clusters[cid]["category"] == "general" else 0.65
)
concept_clusters[cid]["last_prompt_label"] = phrase_label
current_concept_labels[cid] = phrase_label
for w in phrase_words:
@@ -1249,6 +1320,10 @@ class FunPackVideoRefiner:
concept_clusters[concept_id] = cluster
question_type = question_type or "presence"
if question_type == "category":
self._apply_category_feedback(cluster, feedback_rating, iter_num)
return
direct_deltas = {
"presence": {1: 0.90, 2: 0.50, 3: 0.20, 4: -0.15, 5: 0.04, 6: -0.55},
"priority": {1: -0.25, 2: -0.12, 3: -0.05, 4: 0.08, 5: 0.18, 6: 0.30},
@@ -1780,6 +1855,10 @@ class FunPackVideoRefiner:
overlap = len(lora_words & prompt_words) / max(1, len(lora_words)) if lora_words else 0.0
category_score = 0.62 if lora_type == category else 0.0
if lora_type == "concept" and category in {"concept", "details", "subject", "appearance", "action", "environment"}:
category_score = max(category_score, 0.58)
if lora_type == "character" and category in {"character", "subject", "appearance"}:
category_score = max(category_score, 0.72)
if lora_type == "quality" and category == "quality":
category_score = 0.78
if lora_type == "style" and category in {"style", "camera"}:
@@ -2659,7 +2738,8 @@ class FunPackVideoRefiner:
f"[{current_concept_labels.get(cid, c['label'])}/{c.get('category', 'general')}: "
f"top={top_local} | p={c.get('presence_target', 1.0):.2f} "
f"prio={c.get('priority_weight', 1.0):.2f} "
f"stab={c.get('stability_weight', 1.0):.2f}]"
f"stab={c.get('stability_weight', 1.0):.2f} "
f"cat={c.get('category_source', 'auto')}]"
)
concept_line = "; ".join(active_concept_parts) if active_concept_parts else "none"
docs/FunPackGemmaEmbeddingRefiner.md
+2 -2
View File
@@ -38,7 +38,7 @@ If you want the model to preserve speech or spoken lines, wrap that text in `"`
**feedback_enabled**: If enabled, the node can return a concept-focused feedback question alongside the refined conditioning.
**feedback_rating**: Answer to the feedback question on its own 1 to 6 scale. This is only useful when concept feedback is enabled.
**feedback_rating**: Answer to the queued feedback question on its own 1 to 6 scale. The `feedback_question` output includes the exact legend to use. Most questions tune presence, priority, balance, fidelity, stability, or preference. The refiner can also ask a category-discovery question for uncertain word groups: `1=general`, `2=concept`, `3=style`, `4=quality`, `5=character`, `6=details`.
**lora_stack**: Optional stack from `FunPack LoRA Loader`. The refiner uses it to save next-run model LoRA weight suggestions.
@@ -50,7 +50,7 @@ If you want the model to preserve speech or spoken lines, wrap that text in `"`
**status**: Short general node state summary. Use this to quickly see whether the refiner is learning well, what it is focused on, and which subsystems are active.
**feedback_question**: Optional concept feedback prompt generated by the node when feedback mode is active.
**feedback_question**: Optional concept feedback prompt generated by the node when feedback mode is active. The node prioritizes category-discovery questions for unknown or auto-classified concept phrases, then uses later 1 to 6 answers to adjust per-concept weights.
**training_info**: Detailed training diagnostics for the current refinement step, including exact metrics, concept breakdown, LoRA suggestion details, and sigma/latent refinement details.