diff --git a/src/brevitas_examples/stable_diffusion/sd_quant/export.py b/src/brevitas_examples/stable_diffusion/sd_quant/export.py
index 89d846a79..99b77a275 100644
--- a/src/brevitas_examples/stable_diffusion/sd_quant/export.py
+++ b/src/brevitas_examples/stable_diffusion/sd_quant/export.py
@@ -40,18 +40,18 @@ def handle_quant_param(layer, layer_dict):
         layer_dict['output_scale_shape'] = output_scale.shape
     layer_dict['input_scale'] = input_scale.numpy().tolist()
     layer_dict['input_scale_shape'] = input_scale.shape
-    layer_dict['input_zp'] = input_zp.numpy().tolist()
+    layer_dict['input_zp'] = input_zp.to(torch.float32).cpu().numpy().tolist()
     layer_dict['input_zp_shape'] = input_zp.shape
-    layer_dict['input_zp_dtype'] = str(torch.int8)
+    layer_dict['input_zp_dtype'] = str(input_zp.dtype)
     layer_dict['weight_scale'] = weight_scale.cpu().numpy().tolist()
     nelems = layer.weight.shape[0]
     weight_scale_shape = [nelems] + [1] * (layer.weight.data.ndim - 1)
     layer_dict['weight_scale_shape'] = weight_scale_shape
-    if torch.sum(weight_zp) != 0.:
+    if torch.sum(weight_zp.to(torch.float32)) != 0.:
         weight_zp = weight_zp - 128.  # apply offset to have signed z
-        layer_dict['weight_zp'] = weight_zp.cpu().numpy().tolist()
+        layer_dict['weight_zp'] = weight_zp.to(torch.float32).cpu().numpy().tolist()
         layer_dict['weight_zp_shape'] = weight_scale_shape
-        layer_dict['weight_zp_dtype'] = str(torch.int8)
+        layer_dict['weight_zp_dtype'] = str(weight_zp.dtype)
     return layer_dict
 
 
@@ -63,6 +63,8 @@ def export_quant_params(pipe, output_dir, export_vae=False):
         vae_output_path = os.path.join(output_dir, 'vae.safetensors')
         print(f"Saving vae to {vae_output_path} ...")
     from brevitas.export.onnx.standard.qcdq.manager import StdQCDQONNXManager
+    export_manager = StdQCDQONNXManager
+    export_manager.change_weight_export(export_weight_q_node=True) # We're exporting FP weights + quantization parameters
     quant_params = dict()
     state_dict = pipe.unet.state_dict()
     state_dict = {k: v for (k, v) in state_dict.items() if 'tensor_quant' not in k}
@@ -70,7 +72,7 @@ def export_quant_params(pipe, output_dir, export_vae=False):
     state_dict = {k.replace('.layer.', '.'): v for (k, v) in state_dict.items()}
 
     handled_quant_layers = set()
-    with torch.no_grad(), brevitas_proxy_export_mode(pipe.unet, StdQCDQONNXManager):
+    with torch.no_grad(), brevitas_proxy_export_mode(pipe.unet, export_manager):
         for name, module in pipe.unet.named_modules():
             if isinstance(module, EqualizedModule):
                 if id(module.layer) in handled_quant_layers: