From fbf7a7b15a02cb8ce56fe8acfdaefc69871d5bba Mon Sep 17 00:00:00 2001
From: Ethanfel <ethan.fel@ts-pc.fr>
Date: Wed, 4 Mar 2026 16:58:57 +0100
Subject: [PATCH] fix: conflict editor memory leak and SaveMergedLoRA is_clip
 detection

- LoRAConflictEditor: clear loaded_loras after analysis to prevent
  unbounded memory accumulation across runs (100s of MB per large LoRA).
- SaveMergedLoRA: iterate model/clip patches separately with explicit
  is_clip flag instead of checking membership in clip_patches dict.

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
---
 lora_optimizer.py | 48 +++++++++++++++++++++++++----------------------
 1 file changed, 26 insertions(+), 22 deletions(-)

diff --git a/lora_optimizer.py b/lora_optimizer.py
index de5ae78..166278b 100644
--- a/lora_optimizer.py
+++ b/lora_optimizer.py
@@ -3009,31 +3009,31 @@ class SaveMergedLoRA:
 
         state_dict = {}
 
-        for target_key, patch in list(model_patches.items()) + list(clip_patches.items()):
-            is_clip = target_key in clip_patches
-            tkey = target_key[0] if isinstance(target_key, tuple) else target_key
-            lora_prefix = key_map.get(tkey, tkey)
+        for is_clip, patches in [(False, model_patches), (True, clip_patches)]:
+            for target_key, patch in patches.items():
+                tkey = target_key[0] if isinstance(target_key, tuple) else target_key
+                lora_prefix = key_map.get(tkey, tkey)
 
-            if isinstance(patch, LoRAAdapter):
-                mat_up, mat_down, alpha, mid, _, _ = patch.weights
-                alpha = float(alpha) if alpha is not None else float(mat_down.shape[0])
-            elif isinstance(patch, tuple) and len(patch) == 2 and patch[0] == "diff":
-                diff_tensor = patch[1][0]
-                rank = fallback_rank if auto_rank else save_rank
-                compressed = LoRAOptimizer._compress_to_lowrank(diff_tensor, rank)
-                mat_up, mat_down, alpha, mid, _, _ = compressed.weights
-                alpha = float(alpha)
-            else:
-                logging.warning(f"[Save Merged LoRA] Skipping unknown patch type for {lora_prefix}: {type(patch)}")
-                continue
+                if isinstance(patch, LoRAAdapter):
+                    mat_up, mat_down, alpha, mid, _, _ = patch.weights
+                    alpha = float(alpha) if alpha is not None else float(mat_down.shape[0])
+                elif isinstance(patch, tuple) and len(patch) == 2 and patch[0] == "diff":
+                    diff_tensor = patch[1][0]
+                    rank = fallback_rank if auto_rank else save_rank
+                    compressed = LoRAOptimizer._compress_to_lowrank(diff_tensor, rank)
+                    mat_up, mat_down, alpha, mid, _, _ = compressed.weights
+                    alpha = float(alpha)
+                else:
+                    logging.warning(f"[Save Merged LoRA] Skipping unknown patch type for {lora_prefix}: {type(patch)}")
+                    continue
 
-            if bake_strength:
-                strength = clip_strength if is_clip else output_strength
-                alpha *= strength
+                if bake_strength:
+                    strength = clip_strength if is_clip else output_strength
+                    alpha *= strength
 
-            state_dict[f"{lora_prefix}.lora_up.weight"] = mat_up.contiguous()
-            state_dict[f"{lora_prefix}.lora_down.weight"] = mat_down.contiguous()
-            state_dict[f"{lora_prefix}.alpha"] = torch.tensor(alpha)
+                state_dict[f"{lora_prefix}.lora_up.weight"] = mat_up.contiguous()
+                state_dict[f"{lora_prefix}.lora_down.weight"] = mat_down.contiguous()
+                state_dict[f"{lora_prefix}.alpha"] = torch.tensor(alpha)
 
         save_file(state_dict, save_path)
         logging.info(f"[Save Merged LoRA] Saved {len(state_dict) // 3} LoRA keys to {save_path}")
@@ -3383,6 +3383,10 @@ class LoRAConflictEditor(_LoRAMergeBase):
             prefixes_analyzed,
         )
 
+        # Free loaded LoRA state dicts — the editor only needs them during analysis,
+        # and keeping them would leak hundreds of MB per run for large models.
+        self.loaded_loras.clear()
+
         return (enriched, report, resolved_strategy)
 
     @staticmethod