Merge pull request #243 from modelscope/flux-lora

support preset lora

examples/train/flux/train_flux_lora.py

@@ -8,7 +8,7 @@ os.environ["TOKENIZERS_PARALLELISM"] = "True"
 class LightningModel(LightningModelForT2ILoRA):
     def __init__(
         self,
-        torch_dtype=torch.float16, pretrained_weights=[],
+        torch_dtype=torch.float16, pretrained_weights=[], preset_lora_path=None,
         learning_rate=1e-4, use_gradient_checkpointing=True,
         lora_rank=4, lora_alpha=4, lora_target_modules="to_q,to_k,to_v,to_out", init_lora_weights="kaiming",
         state_dict_converter=None, quantize = None
@@ -21,6 +21,8 @@ class LightningModel(LightningModelForT2ILoRA):
         else:
             model_manager.load_models(pretrained_weights[1:])
             model_manager.load_model(pretrained_weights[0], torch_dtype=quantize)
+        if preset_lora_path is not None:
+            model_manager.load_lora(preset_lora_path)
             
         self.pipe = FluxImagePipeline.from_model_manager(model_manager)
         
@@ -82,6 +84,12 @@ def parse_args():
         choices=["float8_e4m3fn"],
         help="Whether to use quantization when training the model, and in which format.",
     )
+    parser.add_argument(
+        "--preset_lora_path",
+        type=str,
+        default=None,
+        help="Preset LoRA path.",
+    )
     parser = add_general_parsers(parser)
     args = parser.parse_args()
     return args
@@ -92,6 +100,7 @@ if __name__ == '__main__':
     model = LightningModel(
         torch_dtype={"32": torch.float32, "bf16": torch.bfloat16}.get(args.precision, torch.float16),
         pretrained_weights=[args.pretrained_dit_path, args.pretrained_text_encoder_path, args.pretrained_text_encoder_2_path, args.pretrained_vae_path],
+        preset_lora_path=args.preset_lora_path,
         learning_rate=args.learning_rate,
         use_gradient_checkpointing=args.use_gradient_checkpointing,
         lora_rank=args.lora_rank,