diff --git a/modules/sd_models.py b/modules/sd_models.py
index 2f12a1684..bebd745cd 100644
--- a/modules/sd_models.py
+++ b/modules/sd_models.py
@@ -305,7 +305,7 @@ def load_diffuser_force(model_type, checkpoint_info, diffusers_load_config, op='
         elif model_type in ['AuraFlow']: # forced pipeline
             from pipelines.model_auraflow import load_auraflow
             sd_model = load_auraflow(checkpoint_info, diffusers_load_config)
-            allow_post_quant = True
+            allow_post_quant = False
         elif model_type in ['FLUX']:
             from pipelines.model_flux import load_flux
             sd_model = load_flux(checkpoint_info, diffusers_load_config)
@@ -381,7 +381,7 @@ def load_diffuser_force(model_type, checkpoint_info, diffusers_load_config, op='
         elif model_type in ['Kandinsky 2.2']:
             from pipelines.model_kandinsky import load_kandinsky22
             sd_model = load_kandinsky22(checkpoint_info, diffusers_load_config)
-            allow_post_quant = False
+            allow_post_quant = True
         elif model_type in ['Kandinsky 3.0']:
             from pipelines.model_kandinsky import load_kandinsky3
             sd_model = load_kandinsky3(checkpoint_info, diffusers_load_config)
diff --git a/modules/sdnq/__init__.py b/modules/sdnq/__init__.py
index 548a3bd34..07eaf0976 100644
--- a/modules/sdnq/__init__.py
+++ b/modules/sdnq/__init__.py
@@ -90,7 +90,7 @@ def sdnq_quantize_layer(layer, weights_dtype="int8", torch_dtype=None, group_siz
             try:
                 output_channel_size, channel_size = layer.weight.shape
             except Exception as e:
-                raise ValueError(f"SDNQ: layer_class_name={layer_class_name} layer_weight_shape={layer.weight.shape} weights_dtype={weights_dtype} unsupported") from e
+                raise ValueError(f"SDNQ: param_name={param_name} layer_class_name={layer_class_name} layer_weight_shape={layer.weight.shape} weights_dtype={weights_dtype} unsupported") from e
             if use_quantized_matmul:
                 use_quantized_matmul = weights_dtype in quantized_matmul_dtypes and channel_size >= 32 and output_channel_size >= 32
                 if use_quantized_matmul:
@@ -376,6 +376,7 @@ class SDNQQuantizer(DiffusersQuantizer):
         keep_in_fp32_modules: List[str] = [],
         **kwargs, # pylint: disable=unused-argument
     ):
+        print("AAAAA:", model.__class__.__name__, "|", getattr(model, "_keep_in_fp32_modules", "None"))
         if keep_in_fp32_modules is not None:
             self.modules_to_not_convert.extend(keep_in_fp32_modules)
         elif getattr(model, "_keep_in_fp32_modules", None) is not None: