[WIP][refactor quanizers][step 1] refactor rtn and tuning

auto_round/compressors/diffusion/compressor.py

@@ -172,18 +172,6 @@ def _update_inputs(self, inputs: dict, q_inputs: dict) -> tuple[dict, dict]:
             q_inputs = {k: q_inputs.pop(k, None) for k in input_id_str}
         return inputs, q_inputs
 
-    def _split_inputs(self, inputs: dict, first_input_name: str) -> tuple[dict, dict]:
-        input_id_str = [key for key in inputs.keys() if "hidden_state" in key]
-        input_ids = {k: inputs.pop(k, None) for k in input_id_str}
-        input_others = inputs
-        return input_ids, input_others
-
-    def _get_current_output(self, output: dict, indices: list[int]) -> torch.Tensor:
-        assert "hidden_states" in output
-        current_output = [output["hidden_states"][x] for x in indices]
-        current_output = torch.cat(current_output, dim=self.batch_dim)
-        return current_output
-
     def _get_current_q_output(
         self,
         block: torch.nn.Module,

auto_round/quantizers/__init__.py

@@ -0,0 +1,15 @@
+# Copyright (c) 2026 Intel Corporation
+#
+# 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.
+
+from auto_round.quantizers.entrypoint import create_quantizers