diff --git a/modules/model_quant.py b/modules/model_quant.py
index 8488401a8..499e00e92 100644
--- a/modules/model_quant.py
+++ b/modules/model_quant.py
@@ -508,3 +508,16 @@ def get_dit_args(load_config:dict={}, module:str=None, device_map:bool=False, al
     else:
         quant_args = {}
     return config, quant_args
+
+
+def do_post_load_quant(sd_model):
+    from modules import shared
+    if shared.opts.nncf_compress_weights and not (shared.opts.cuda_compile and shared.opts.cuda_compile_backend == "openvino_fx"):
+        sd_model = nncf_compress_weights(sd_model)
+    if shared.opts.optimum_quanto_weights:
+        sd_model = optimum_quanto_weights(sd_model)
+    if shared.opts.torchao_quantization and shared.opts.torchao_quantization_mode == 'post':
+        sd_model = torchao_quantization(sd_model)
+    if shared.opts.layerwise_quantization:
+        apply_layerwise(sd_model)
+    return sd_model
diff --git a/modules/sd_models.py b/modules/sd_models.py
index 35507dcec..7d528c693 100644
--- a/modules/sd_models.py
+++ b/modules/sd_models.py
@@ -146,8 +146,6 @@ def set_diffuser_options(sd_model, vae=None, op:str='model', offload:bool=True,
                 model.eval()
             return model
         sd_model = apply_function_to_model(sd_model, eval_model, ["Model", "VAE", "TE"], op="eval")
-    if len(shared.opts.torchao_quantization) > 0 and shared.opts.torchao_quantization_mode == 'post':
-        sd_model = model_quant.torchao_quantization(sd_model)
 
     if shared.opts.opt_channelslast and hasattr(sd_model, 'unet'):
         shared.log.quiet(quiet, f'Setting {op}: channels-last=True')
@@ -602,12 +600,7 @@ def load_diffuser(checkpoint_info=None, already_loaded_state_dict=None, timer=No
         prompt_parser_diffusers.cache.clear()
 
         set_diffuser_options(sd_model, vae, op, offload=False)
-        if shared.opts.nncf_compress_weights and not (shared.opts.cuda_compile and shared.opts.cuda_compile_backend == "openvino_fx"):
-            sd_model = model_quant.nncf_compress_weights(sd_model) # run this before move model so it can be compressed in CPU
-        if shared.opts.optimum_quanto_weights:
-            sd_model = model_quant.optimum_quanto_weights(sd_model) # run this before move model so it can be compressed in CPU
-        if shared.opts.layerwise_quantization:
-            model_quant.apply_layerwise(sd_model)
+        sd_model = model_quant.do_post_load_quant(sd_model) # run this before move model so it can be compressed in CPU
         timer.record("options")
 
         set_diffuser_offload(sd_model, op)