feat: add LoRA support for lumina minimal inference

lumina_minimal_inference.py

@@ -27,6 +27,7 @@ from library import (
     sd3_train_utils,
     strategy_lumina,
 )
+import networks.lora_lumina as lora_lumina
 from library.device_utils import get_preferred_device, init_ipex
 from library.utils import setup_logging, str_to_dtype
 
@@ -248,6 +249,14 @@ def setup_parser() -> argparse.ArgumentParser:
         action="store_true",
         help="Use sage attention for Lumina model",
     )
+    parser.add_argument(
+        "--lora_weights",
+        type=str,
+        nargs="*",
+        default=[],
+        help="LoRA weights, each argument is a `path;multiplier` (semi-colon separated)",
+    )
+    parser.add_argument("--merge_lora_weights", action="store_true", help="Merge LoRA weights to model")
     return parser
 
 
@@ -275,6 +284,30 @@ if __name__ == "__main__":
     # Load Autoencoder
     ae = lumina_util.load_ae(args.ae_path, dtype=None, device="cpu")
 
+    # LoRA
+    lora_models = []
+    for weights_file in args.lora_weights:
+        if ";" in weights_file:
+            weights_file, multiplier = weights_file.split(";")
+            multiplier = float(multiplier)
+        else:
+            multiplier = 1.0
+
+        weights_sd = load_file(weights_file)
+        lora_model, _ = lora_lumina.create_network_from_weights(
+            multiplier, None, ae, [gemma2], model, weights_sd, True
+        )
+
+        if args.merge_lora_weights:
+            lora_model.merge_to([gemma2], model, weights_sd)
+        else:
+            lora_model.apply_to([gemma2], model)
+            info = lora_model.load_state_dict(weights_sd, strict=True)
+            logger.info(f"Loaded LoRA weights from {weights_file}: {info}")
+            lora_model.eval()
+
+        lora_models.append(lora_model)
+
     generate_image(
         model,
         gemma2,