diff --git a/api/onnx_web/convert/diffusion/diffusion.py b/api/onnx_web/convert/diffusion/diffusion.py
index bdcd90bc..55eb571f 100644
--- a/api/onnx_web/convert/diffusion/diffusion.py
+++ b/api/onnx_web/convert/diffusion/diffusion.py
@@ -833,6 +833,16 @@ def convert_diffusion_diffusers_optimum(
         logger.debug("exporting torch model for %s: %s", source, temp_path)
         pipeline.save_pretrained(temp_path)
 
+    # config needed for ControlNet later
+    num_tokens = pipeline.text_encoder.config.max_position_embeddings
+    text_hidden_size = pipeline.text_encoder.config.hidden_size
+    unet_in_channels = pipeline.unet.config.in_channels
+    unet_sample_size = pipeline.unet.config.sample_size
+
+    # GC temporary pipeline
+    del pipeline
+    run_gc()
+
     main_export(
         temp_path,
         output=dest_path,
@@ -868,4 +878,38 @@ def convert_diffusion_diffusers_optimum(
             location=ONNX_WEIGHTS,
         )
 
+    if conversion.control:
+        logger.debug("converting CNet from pretrained UNet")
+        cnet_path = convert_diffusion_diffusers_cnet(
+            conversion,
+            name,
+            temp_path,
+            device,
+            Path(dest_path),
+            dtype,
+            unet_in_channels,
+            unet_sample_size,
+            num_tokens,
+            text_hidden_size,
+            v2=v2,
+        )
+
+        if conversion.half:
+            infer_shapes_path(cnet_path)
+            cnet = load_model(cnet_path)
+            opt_model = convert_float_to_float16(
+                cnet,
+                disable_shape_infer=True,
+                force_fp16_initializers=True,
+                keep_io_types=True,
+                op_block_list=["Attention", "MultiHeadAttention"],
+            )
+            save_model(
+                opt_model,
+                cnet_path,
+                save_as_external_data=True,
+                all_tensors_to_one_file=True,
+                location=ONNX_WEIGHTS,
+            )
+
     return (True, dest_path)