nodes/olivv-cs-ComfyUI-FunPack
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This commit is contained in:
aimfordeb
2026-04-21 17:20:00 +03:00
committed by GitHub
parent 3e234b8e23
commit 57fcb772b6
1 changed files with 45 additions and 30 deletions
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funpack.py
+45 -30
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@@ -527,7 +527,8 @@ class FunPackGemmaEmbeddingRefiner:
return score * self._get_question_base_weight(question_type, category) * freshness
def _select_feedback_question(self, ordered_concept_ids: list, concept_clusters: dict,
concept_groups: dict, last_rating: int, rating: int,
concept_groups: dict, current_concept_labels: dict,
last_rating: int, rating: int,
similarity: float, iter_num: int):
if not ordered_concept_ids:
return None
@@ -565,7 +566,7 @@ class FunPackGemmaEmbeddingRefiner:
)
candidates.append({
"concept_id": cid,
"concept_label": cluster.get("label", ""),
"concept_label": current_concept_labels.get(cid, cluster.get("label", "")),
"question_type": question_type,
"neighbor_ids": neighbor_ids,
"group_id": chosen_group_id,
@@ -632,7 +633,8 @@ class FunPackGemmaEmbeddingRefiner:
return fallback_id
def _force_feedback_fallback(self, ordered_concept_ids: list, concept_clusters: dict,
concept_groups: dict, rating_shift: float, similarity: float):
concept_groups: dict, current_concept_labels: dict,
rating_shift: float, similarity: float):
if not ordered_concept_ids:
return None
@@ -668,7 +670,7 @@ class FunPackGemmaEmbeddingRefiner:
return {
"concept_id": chosen_cid,
"concept_label": cluster.get("label", ""),
"concept_label": current_concept_labels.get(chosen_cid, cluster.get("label", "")),
"question_type": question_type,
"neighbor_ids": self._get_concept_neighbors(chosen_cid, ordered_concept_ids, radius=2),
"group_id": chosen_group_id,
@@ -703,7 +705,7 @@ class FunPackGemmaEmbeddingRefiner:
new_id = md5("|".join(sorted(phrase_words)).encode()).hexdigest()[:10]
return new_id, True
def _build_word_concept_map(self, prompt: str, concept_clusters: dict):
def _build_word_concept_map(self, prompt: str, concept_clusters: dict):
"""
Parse the prompt into concept phrases (Level 3), match or create clusters,
and return:
@@ -713,12 +715,14 @@ class FunPackGemmaEmbeddingRefiner:
Updates concept_clusters in-place. The ordered list is the backbone for
group building and neighbour lookup at Level 4.
"""
word_to_concept = {}
ordered_concept_ids = []
word_to_concept = {}
ordered_concept_ids = []
current_concept_labels = {}
for phrase_words in self._parse_concepts(prompt):
cid, is_new = self._match_concept(phrase_words, concept_clusters)
if cid is None:
continue
phrase_label = " ".join(phrase_words[:6])
if is_new:
concept_clusters[cid] = self._default_concept_cluster(phrase_words)
else:
@@ -732,14 +736,17 @@ class FunPackGemmaEmbeddingRefiner:
concept_clusters[cid]["category"] = self._infer_concept_category(
concept_clusters[cid]["anchor_words"]
)
concept_clusters[cid]["last_prompt_label"] = phrase_label
current_concept_labels[cid] = phrase_label
for w in phrase_words:
word_to_concept[w] = cid
if cid not in ordered_concept_ids:
ordered_concept_ids.append(cid)
return word_to_concept, ordered_concept_ids
def _build_concept_groups(self, ordered_concept_ids: list, concept_clusters: dict,
existing_groups: dict, window: int = 3):
return word_to_concept, ordered_concept_ids, current_concept_labels
def _build_concept_groups(self, ordered_concept_ids: list, concept_clusters: dict,
existing_groups: dict, current_concept_labels: dict,
window: int = 3):
"""
Level 4: group consecutive concept phrases into semantic sentence-level
units using a non-overlapping sliding window of size `window`.
@@ -770,14 +777,18 @@ class FunPackGemmaEmbeddingRefiner:
existing_gid = gid
break
if existing_gid:
# Preserve the canonical prompt order even if it shifted slightly
groups[existing_gid]["concept_ids"] = chunk
else:
labels = [concept_clusters[cid]["label"]
for cid in chunk if cid in concept_clusters]
gid = md5("|".join(chunk).encode()).hexdigest()[:10]
groups[gid] = {
if existing_gid:
# Preserve the canonical prompt order even if it shifted slightly
groups[existing_gid]["concept_ids"] = chunk
groups[existing_gid]["label"] = " | ".join(
current_concept_labels.get(cid, concept_clusters.get(cid, {}).get("label", cid))
for cid in chunk
)
else:
labels = [current_concept_labels.get(cid, concept_clusters[cid]["label"])
for cid in chunk if cid in concept_clusters]
gid = md5("|".join(chunk).encode()).hexdigest()[:10]
groups[gid] = {
"label": " | ".join(labels),
"concept_ids": chunk,
"reward_ema": 0.0,
@@ -1155,16 +1166,17 @@ class FunPackGemmaEmbeddingRefiner:
# ====================== CONCEPT CLUSTER + GROUP SETUP (Levels 3 & 4) ======================
concept_clusters = global_adaptive["concept_clusters"]
if analysis_prompt:
word_to_concept, ordered_concept_ids = self._build_word_concept_map(
word_to_concept, ordered_concept_ids, current_concept_labels = self._build_word_concept_map(
analysis_prompt, concept_clusters
)
else:
word_to_concept, ordered_concept_ids = {}, []
word_to_concept, ordered_concept_ids, current_concept_labels = {}, [], {}
if feedback_enabled and not ordered_concept_ids and analysis_prompt:
fallback_cid = self._build_prompt_fallback_concept(analysis_prompt, concept_clusters)
if fallback_cid:
ordered_concept_ids = [fallback_cid]
current_concept_labels[fallback_cid] = concept_clusters[fallback_cid].get("label", analysis_prompt[:64])
for w in concept_clusters[fallback_cid].get("anchor_words", []):
word_to_concept.setdefault(w, fallback_cid)
@@ -1175,10 +1187,10 @@ class FunPackGemmaEmbeddingRefiner:
json.dump(data, f, indent=2)
# Build or update concept groups from the current ordered concept list
concept_groups = self._build_concept_groups(
ordered_concept_ids, concept_clusters,
global_adaptive["concept_groups"], window=3
)
concept_groups = self._build_concept_groups(
ordered_concept_ids, concept_clusters,
global_adaptive["concept_groups"], current_concept_labels, window=3
)
global_adaptive["concept_groups"] = concept_groups
# Track which groups are active in this iteration
@@ -1462,6 +1474,7 @@ class FunPackGemmaEmbeddingRefiner:
ordered_concept_ids,
concept_clusters,
concept_groups,
current_concept_labels,
last_rating,
rating,
similarity,
@@ -1473,6 +1486,7 @@ class FunPackGemmaEmbeddingRefiner:
ordered_concept_ids,
concept_clusters,
concept_groups,
current_concept_labels,
rating_shift,
similarity
)
@@ -1522,7 +1536,7 @@ class FunPackGemmaEmbeddingRefiner:
c = concept_clusters[cid]
top_local = self._get_top_tokens(c["word_importance"], tokenizer, 4)
active_concept_parts.append(
f"[{c['label']}/{c.get('category', 'general')}: "
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}]"
@@ -1540,10 +1554,11 @@ class FunPackGemmaEmbeddingRefiner:
group_line = "; ".join(group_parts) if group_parts else "none"
# Dominant concept: the phrase the model currently weights most heavily
dom_cid, dom_score, dom_label = self._get_dominant_concept(
ordered_concept_ids, concept_clusters
)
dominant_line = f"'{dom_label}' (avg_imp={dom_score:.2f})" if dom_cid else "undetermined"
dom_cid, dom_score, dom_label = self._get_dominant_concept(
ordered_concept_ids, concept_clusters
)
dom_label = current_concept_labels.get(dom_cid, dom_label) if dom_cid else dom_label
dominant_line = f"'{dom_label}' (avg_imp={dom_score:.2f})" if dom_cid else "undetermined"
normalized_reward = max(0.0, min(1.0, (avg_reward_ema + 1.0) / 2.0))
stability_factor = max(0.0, 1.0 - similarity)