nodes/ethanfel-ComfyUI-LoRA-Optimizer
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fix: reuse AutoTuner cache across output_mode switches

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Remove output_mode from the internal cache key so switching between
merge and tuning_only reuses the cached sweep results. The cache
stores (result, mode) tuples — on hit, adapts the return based on
current output_mode without re-running the sweep.

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
This commit is contained in:
Ethanfel
2026-03-07 03:11:52 +01:00
co-authored by Claude Opus 4.6
parent ac7010390c
commit 71d103af30
1 changed files with 14 additions and 7 deletions
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lora_optimizer.py
+14 -7
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@@ -4709,17 +4709,24 @@ class LoRAAutoTuner(LoRAOptimizer):
sort_keys=True)
lora_hash = hashlib.sha256(hash_input.encode()).hexdigest()[:16]
# Check AutoTuner cache
# Check AutoTuner cache (output_mode excluded so mode switches reuse cached sweep)
at_cache_key = hashlib.sha256(
f"{lora_hash}|os={output_strength}|csm={clip_strength_multiplier}"
f"|top_n={top_n}|nk={normalize_keys}|ss={scoring_svd}"
f"|ap={architecture_preset}|vb={vram_budget}"
f"|spd={scoring_speed}|om={output_mode}".encode()
f"|spd={scoring_speed}".encode()
).hexdigest()[:16]
if cache_patches == "enabled" and hasattr(self, '_autotuner_cache') and at_cache_key in self._autotuner_cache:
cached = self._autotuner_cache[at_cache_key]
logging.info(f"[LoRA AutoTuner] Using cached result")
return cached
cached_result, cached_mode = self._autotuner_cache[at_cache_key]
if output_mode == "tuning_only":
# Return base model + cached tuner_data (no merge needed)
logging.info("[LoRA AutoTuner] Using cached result (tuning_only passthrough)")
return (model, clip, cached_result[2], "", cached_result[4], None)
elif cached_mode == "merge":
# Cached has merged model, return as-is
logging.info("[LoRA AutoTuner] Using cached result")
return cached_result
# else: cached was tuning_only but now need merge — fall through to re-run
# --- Pass 1: Analysis (run once, reuse for all configs) ---
model_keys = self._get_model_keys(model)
@@ -5139,7 +5146,7 @@ class LoRAAutoTuner(LoRAOptimizer):
logging.info("[LoRA AutoTuner] tuning_only mode — returning base model (no merge)")
result = (model, clip, report, "", tuner_data, None)
if cache_patches == "enabled":
self._autotuner_cache = {at_cache_key: result}
self._autotuner_cache = {at_cache_key: (result, "tuning_only")}
else:
self._autotuner_cache = {}
return result
@@ -5160,7 +5167,7 @@ class LoRAAutoTuner(LoRAOptimizer):
result = (ret_model, ret_clip, report, ret_analysis_report, tuner_data, ret_lora_data)
if cache_patches == "enabled":
self._autotuner_cache = {at_cache_key: result}
self._autotuner_cache = {at_cache_key: (result, "merge")}
else:
self._autotuner_cache = {}
logging.info("[LoRA AutoTuner] Patch cache disabled — RAM freed after merge")