diff --git a/modules/sdnq/__init__.py b/modules/sdnq/__init__.py
index bca210c1b..e88bfc8d2 100644
--- a/modules/sdnq/__init__.py
+++ b/modules/sdnq/__init__.py
@@ -4,6 +4,7 @@ from typing import Any, Dict, List, Tuple, Optional, Union
 from dataclasses import dataclass
 from enum import Enum
 
+import re
 import torch
 from diffusers.quantizers.base import DiffusersQuantizer
 from diffusers.quantizers.quantization_config import QuantizationConfigMixin
@@ -270,7 +271,11 @@ class SDNQQuantizer(DiffusersQuantizer):
     ):
         if param_name.endswith(".weight"):
             split_param_name = param_name.split(".")
-            if param_name not in self.modules_to_not_convert and not any(param in split_param_name for param in self.modules_to_not_convert):
+            if (
+                param_name not in self.modules_to_not_convert
+                and not any(param in split_param_name for param in self.modules_to_not_convert)
+                and not any("*" in param and re.match(param.replace("*", ".*"), param_name) for param in self.modules_to_not_convert)
+            ):
                 layer_class_name = get_module_from_name(model, param_name)[0].__class__.__name__
                 if layer_class_name in allowed_types:
                     if layer_class_name in conv_types or layer_class_name in conv_transpose_types:
@@ -303,7 +308,11 @@ class SDNQQuantizer(DiffusersQuantizer):
         if len(self.quantization_config.modules_dtype_dict.keys()) > 0:
             split_param_name = param_name.split(".")
             for key, value in self.quantization_config.modules_dtype_dict.items():
-                if param_name in value or any(param in split_param_name for param in value):
+                if (
+                    param_name in value
+                    or any(param in split_param_name for param in value)
+                    or any("*" in param and re.match(param.replace("*", ".*"), param_name) for param in value)
+                ):
                     key = key.lower()
                     if key in {"8bit", "8bits"}:
                         if dtype_dict[weights_dtype]["num_bits"] != 8:
diff --git a/pipelines/model_qwen.py b/pipelines/model_qwen.py
index 9c7984dd5..b6e57c95f 100644
--- a/pipelines/model_qwen.py
+++ b/pipelines/model_qwen.py
@@ -30,7 +30,7 @@ def load_qwen(checkpoint_info, diffusers_load_config={}):
         #     cls_name = nunchaku.pipeline.pipeline_qwenimage.NunchakuQwenImagePipeline # we dont need this
 
     if transformer is None:
-        transformer = generic.load_transformer(repo_id, cls_name=diffusers.QwenImageTransformer2DModel, load_config=diffusers_load_config, modules_dtype_dict={"minimum_6bit": ["img_mod", "pos_embed", "time_text_embed", "img_in", "txt_in", "norm_out"]})
+        transformer = generic.load_transformer(repo_id, cls_name=diffusers.QwenImageTransformer2DModel, load_config=diffusers_load_config, modules_dtype_dict={"minimum_6bit": ["pos_embed", "time_text_embed", "img_in", "txt_in", "norm_out", "transformer_blocks.0.img_mod.1.weight", "transformer_blocks.59.img_mod.1.weight"]})
 
     repo_te = 'Qwen/Qwen-Image' # if 'Qwen-Lightning' in repo_id or 'Qwen-Image-Edit' in repo_id else repo_id
     text_encoder = generic.load_text_encoder(repo_te, cls_name=transformers.Qwen2_5_VLForConditionalGeneration, load_config=diffusers_load_config)