diff --git a/src/diffusers/loaders/peft.py b/src/diffusers/loaders/peft.py
index 24393a18836f..da038b9fdca5 100644
--- a/src/diffusers/loaders/peft.py
+++ b/src/diffusers/loaders/peft.py
@@ -63,6 +63,9 @@ def _maybe_adjust_config(config):
     method removes the ambiguity by following what is described here:
     https://github.com/huggingface/diffusers/pull/9985#issuecomment-2493840028.
     """
+    # Track keys that have been explicitly removed to prevent re-adding them.
+    deleted_keys = set()
+
     rank_pattern = config["rank_pattern"].copy()
     target_modules = config["target_modules"]
     original_r = config["r"]
@@ -80,21 +83,22 @@ def _maybe_adjust_config(config):
         ambiguous_key = key
 
         if exact_matches and substring_matches:
-            # if ambiguous we update the rank associated with the ambiguous key (`proj_out`, for example)
+            # if ambiguous, update the rank associated with the ambiguous key (`proj_out`, for example)
             config["r"] = key_rank
-            # remove the ambiguous key from `rank_pattern` and update its rank to `r`, instead
+            # remove the ambiguous key from `rank_pattern` and record it as deleted
             del config["rank_pattern"][key]
+            deleted_keys.add(key)
+            # For substring matches, add them with the original rank only if they haven't been assigned already
             for mod in substring_matches:
-                # avoid overwriting if the module already has a specific rank
-                if mod not in config["rank_pattern"]:
+                if mod not in config["rank_pattern"] and mod not in deleted_keys:
                     config["rank_pattern"][mod] = original_r
 
-            # update the rest of the keys with the `original_r`
+            # Update the rest of the target modules with the original rank if not already set and not deleted
             for mod in target_modules:
-                if mod != ambiguous_key and mod not in config["rank_pattern"]:
+                if mod != ambiguous_key and mod not in config["rank_pattern"] and mod not in deleted_keys:
                     config["rank_pattern"][mod] = original_r
 
-    # handle alphas to deal with cases like
+    # Handle alphas to deal with cases like:
     # https://github.com/huggingface/diffusers/pull/9999#issuecomment-2516180777
     has_different_ranks = len(config["rank_pattern"]) > 1 and list(config["rank_pattern"])[0] != config["r"]
     if has_different_ranks:
@@ -187,6 +191,11 @@ def load_lora_adapter(self, pretrained_model_name_or_path_or_dict, prefix="trans
         from peft import LoraConfig, inject_adapter_in_model, set_peft_model_state_dict
         from peft.tuners.tuners_utils import BaseTunerLayer
 
+        try:
+            from peft.utils.constants import FULLY_QUALIFIED_PATTERN_KEY_PREFIX
+        except ImportError:
+            FULLY_QUALIFIED_PATTERN_KEY_PREFIX = None
+
         cache_dir = kwargs.pop("cache_dir", None)
         force_download = kwargs.pop("force_download", False)
         proxies = kwargs.pop("proxies", None)
@@ -251,14 +260,22 @@ def load_lora_adapter(self, pretrained_model_name_or_path_or_dict, prefix="trans
                 # Cannot figure out rank from lora layers that don't have atleast 2 dimensions.
                 # Bias layers in LoRA only have a single dimension
                 if "lora_B" in key and val.ndim > 1:
-                    rank[key] = val.shape[1]
+                    # Support to handle cases where layer patterns are treated as full layer names
+                    # was added later in PEFT. So, we handle it accordingly.
+                    # TODO: when we fix the minimal PEFT version for Diffusers,
+                    # we should remove `_maybe_adjust_config()`.
+                    if FULLY_QUALIFIED_PATTERN_KEY_PREFIX:
+                        rank[f"{FULLY_QUALIFIED_PATTERN_KEY_PREFIX}{key}"] = val.shape[1]
+                    else:
+                        rank[key] = val.shape[1]
 
             if network_alphas is not None and len(network_alphas) >= 1:
                 alpha_keys = [k for k in network_alphas.keys() if k.startswith(f"{prefix}.")]
                 network_alphas = {k.replace(f"{prefix}.", ""): v for k, v in network_alphas.items() if k in alpha_keys}
 
             lora_config_kwargs = get_peft_kwargs(rank, network_alpha_dict=network_alphas, peft_state_dict=state_dict)
-            lora_config_kwargs = _maybe_adjust_config(lora_config_kwargs)
+            if not FULLY_QUALIFIED_PATTERN_KEY_PREFIX:
+                lora_config_kwargs = _maybe_adjust_config(lora_config_kwargs)
 
             if "use_dora" in lora_config_kwargs:
                 if lora_config_kwargs["use_dora"]: