Generate guidance for flux (#2104)

generate guidance

optimum/onnxruntime/modeling_diffusion.py

@@ -437,8 +437,21 @@ def to(self, device: Union[torch.device, str, int]):
     def _load_config(cls, config_name_or_path: Union[str, os.PathLike], **kwargs):
         return cls.load_config(config_name_or_path, **kwargs)
 
-    def _save_config(self, save_directory):
-        self.save_config(save_directory)
+    def _save_config(self, save_directory: Union[str, Path]):
+        model_dir = (
+            self.model_save_dir
+            if not isinstance(self.model_save_dir, TemporaryDirectory)
+            else self.model_save_dir.name
+        )
+        save_dir = Path(save_directory)
+        original_config = Path(model_dir) / self.config_name
+        if original_config.exists():
+            if not save_dir.exists():
+                save_dir.mkdir(parents=True)
+
+            shutil.copy(original_config, save_dir)
+        else:
+            self.save_config(save_directory)
 
     @property
     def components(self) -> Dict[str, Any]:

optimum/utils/input_generators.py

@@ -1508,6 +1508,7 @@ class DummyFluxTransformerTextInputGenerator(DummyTransformerTextInputGenerator)
     SUPPORTED_INPUT_NAMES = (
         "encoder_hidden_states",
         "pooled_projections",
+        "guidance",
         "txt_ids",
     )
 
@@ -1519,5 +1520,8 @@ def generate(self, input_name: str, framework: str = "pt", int_dtype: str = "int
                 else [self.batch_size, self.sequence_length, 3]
             )
             return self.random_int_tensor(shape, max_value=1, framework=framework, dtype=int_dtype)
+        elif input_name == "guidance":
+            shape = [self.batch_size]
+            return self.random_float_tensor(shape, min_value=0, max_value=1, framework=framework, dtype=float_dtype)
 
         return super().generate(input_name, framework, int_dtype, float_dtype)