nodes/olivv-cs-ComfyUI-FunPack
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Update funpack.py

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This commit is contained in:
aimfordeb
2026-04-18 15:38:31 +03:00
committed by GitHub
parent ffe4601187
commit f00efebf01
1 changed files with 14 additions and 7 deletions
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funpack.py
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@@ -79,6 +79,7 @@ class FunPackGemmaEmbeddingRefiner:
"optional": {
"reset_session": ("BOOLEAN", {"default": False, "label": "Reset Session (clears history)"}),
"exploration_strength": ("FLOAT", {"default": 0.07, "min": 0.0, "max": 0.3, "step": 0.001}),
"token_prioritization_strength": ("FLOAT", {"default": 0.85, "min": 0.0, "max": 1.5, "step": 0.05, "label": "Token Prioritization Strength (0 = uniform)"}),
"similarity_threshold": ("FLOAT", {"default": 0.78, "min": 0.0, "max": 1.0, "step": 0.01, "label": "Similarity Threshold (for cross-prompt merging)"}),
"max_history": ("INT", {"default": 50, "min": 10, "max": 500, "step": 5, "label": "Max History Entries"})
}
@@ -93,7 +94,7 @@ class FunPackGemmaEmbeddingRefiner:
def IS_CHANGED(cls, **kwargs):
return float("nan")
def refine(self, conditioning, rating, refinement_key, reset_session=False, exploration_strength=0.07, similarity_threshold=0.78, max_history=50):
def refine(self, conditioning, rating, refinement_key, reset_session=False, exploration_strength=0.07, token_prioritization_strength=0.85, similarity_threshold=0.78, max_history=50):
base_dir = os.path.dirname(os.path.abspath(__file__))
refinements_dir = os.path.join(base_dir, "refinements")
os.makedirs(refinements_dir, exist_ok=True)
@@ -131,7 +132,6 @@ class FunPackGemmaEmbeddingRefiner:
reference = serializable_to_tensor(data["reference_embeds"])
# Compute similarity between current input and stored reference
ref_mean = reference.mean(dim=1)
cur_mean = raw_embeds.mean(dim=1)
similarity = torch.nn.functional.cosine_similarity(ref_mean, cur_mean, dim=1).item()
@@ -139,7 +139,7 @@ class FunPackGemmaEmbeddingRefiner:
last_rating = data.get("last_rating", 3)
if similarity >= similarity_threshold:
# Same/similar prompt → normal per-prompt refinement
# Same/similar prompt - normal refinement
prev_modified = serializable_to_tensor(data.get("history", [{}])[-1].get("modified_embeds", data["reference_embeds"]) if data.get("history") else data["reference_embeds"])
prev_delta = prev_modified - reference
@@ -158,7 +158,7 @@ class FunPackGemmaEmbeddingRefiner:
new_delta = prev_delta * multiplier + torch.randn_like(prev_delta) * noise_scale
merged_good = 0
else:
# Different conditioning detected → merge good past deltas from history
# Different prompt detected - merge good past deltas
good_deltas = []
if "history" in data:
for entry in data["history"]:
@@ -173,6 +173,13 @@ class FunPackGemmaEmbeddingRefiner:
new_delta = torch.randn_like(reference) * exploration_strength * 0.3
merged_good = 0
# Token prioritization (re-added - crucial as you said)
if token_prioritization_strength > 0.0:
token_deltas = torch.norm(new_delta, dim=-1, keepdim=True)
token_importance = token_deltas / (token_deltas.max() + 1e-8)
boost = 1.0 + token_prioritization_strength * token_importance
new_delta = new_delta * boost
new_modified = reference + new_delta
new_modified = torch.clamp(new_modified, min=-60.0, max=60.0)
@@ -201,12 +208,12 @@ class FunPackGemmaEmbeddingRefiner:
iteration = len(data["history"])
if similarity >= similarity_threshold:
status = f"Iter {iteration} | Rating {rating} (prev {last_rating}) | Sim {similarity:.3f} | History {len(data['history'])}"
status = f"Iter {iteration} | Rating {rating} (prev {last_rating}) | TokPri {token_prioritization_strength:.2f} | Sim {similarity:.3f} | History {len(data['history'])}"
else:
status = f"Iter {iteration} | Rating {rating} | New prompt detected (Sim {similarity:.3f}) | Merged {merged_good} good deltas | History {len(data['history'])}"
status = f"Iter {iteration} | Rating {rating} | New prompt (Sim {similarity:.3f}) | Merged {merged_good} good | TokPri {token_prioritization_strength:.2f} | History {len(data['history'])}"
return (modified_conditioning, status)
# Constants from StoryMem
IMAGE_FACTOR = 28
VIDEO_MIN_PIXELS = 48 * IMAGE_FACTOR * IMAGE_FACTOR # 37,632