Do not mutate the source transformer config in SD3ControlNetModel.from_transformer

src/diffusers/models/controlnets/controlnet_sd3.py

@@ -254,8 +254,8 @@ def _get_pos_embed_from_transformer(self, transformer):
     def from_transformer(
         cls, transformer, num_layers=12, num_extra_conditioning_channels=1, load_weights_from_transformer=True
     ):
-        config = transformer.config
-        config["num_layers"] = num_layers or config.num_layers
+        config = dict(transformer.config)
+        config["num_layers"] = num_layers or transformer.config.num_layers
         config["extra_conditioning_channels"] = num_extra_conditioning_channels
         controlnet = cls.from_config(config)
 

tests/models/controlnets/test_models_controlnet_sd3.py

@@ -0,0 +1,83 @@
+# Copyright 2026 HuggingFace Inc.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import torch
+
+from diffusers import SD3ControlNetModel, SD3Transformer2DModel
+
+from ...testing_utils import enable_full_determinism
+
+
+enable_full_determinism()
+
+
+def get_dummy_transformer():
+    torch.manual_seed(0)
+    return SD3Transformer2DModel(
+        sample_size=4,
+        patch_size=1,
+        in_channels=4,
+        out_channels=4,
+        num_layers=3,
+        attention_head_dim=4,
+        num_attention_heads=2,
+        caption_projection_dim=8,
+        joint_attention_dim=8,
+        pooled_projection_dim=8,
+    )
+
+
+class TestSD3ControlNetModelFromTransformer:
+    def test_from_transformer_does_not_mutate_source_config(self):
+        # Regression: `from_transformer` aliased the transformer's live config and wrote
+        # ControlNet-specific values into it, so building a ControlNet silently changed the
+        # source transformer's `num_layers` and added `extra_conditioning_channels`.
+        transformer = get_dummy_transformer()
+        config_before = dict(transformer.config)
+
+        SD3ControlNetModel.from_transformer(
+            transformer,
+            num_layers=1,
+            num_extra_conditioning_channels=2,
+            load_weights_from_transformer=False,
+        )
+
+        assert dict(transformer.config) == config_before, (
+            "`from_transformer` must not modify the source transformer's config."
+        )
+
+    def test_from_transformer_applies_controlnet_config(self):
+        transformer = get_dummy_transformer()
+
+        controlnet = SD3ControlNetModel.from_transformer(
+            transformer,
+            num_layers=1,
+            num_extra_conditioning_channels=2,
+            load_weights_from_transformer=False,
+        )
+
+        assert controlnet.config.num_layers == 1
+        assert controlnet.config.extra_conditioning_channels == 2
+
+    def test_from_transformer_num_layers_falls_back_to_transformer(self):
+        transformer = get_dummy_transformer()
+
+        controlnet = SD3ControlNetModel.from_transformer(
+            transformer,
+            num_layers=None,
+            num_extra_conditioning_channels=1,
+            load_weights_from_transformer=False,
+        )
+
+        assert controlnet.config.num_layers == transformer.config.num_layers