rebuild base modules

examples/train/stable_diffusion/train_sd_lora.py

@@ -0,0 +1,56 @@
+from diffsynth import ModelManager, SDImagePipeline
+from diffsynth.trainers.text_to_image import LightningModelForT2ILoRA, add_general_parsers, launch_training_task
+import torch, os, argparse
+os.environ["TOKENIZERS_PARALLELISM"] = "True"
+
+
+class LightningModel(LightningModelForT2ILoRA):
+    def __init__(
+        self,
+        torch_dtype=torch.float16, pretrained_weights=[],
+        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"
+    ):
+        super().__init__(learning_rate=learning_rate, use_gradient_checkpointing=use_gradient_checkpointing)
+        # Load models
+        model_manager = ModelManager(torch_dtype=torch_dtype, device=self.device)
+        model_manager.load_models(pretrained_weights)
+        self.pipe = SDImagePipeline.from_model_manager(model_manager)
+        self.pipe.scheduler.set_timesteps(1000)
+
+        self.freeze_parameters()
+        self.add_lora_to_model(self.pipe.denoising_model(), lora_rank=lora_rank, lora_alpha=lora_alpha, lora_target_modules=lora_target_modules)
+
+
+def parse_args():
+    parser = argparse.ArgumentParser(description="Simple example of a training script.")
+    parser.add_argument(
+        "--pretrained_path",
+        type=str,
+        default=None,
+        required=True,
+        help="Path to pretrained model. For example, `models/stable_diffusion/v1-5-pruned-emaonly.safetensors`.",
+    )
+    parser.add_argument(
+        "--lora_target_modules",
+        type=str,
+        default="to_q,to_k,to_v,to_out",
+        help="Layers with LoRA modules.",
+    )
+    parser = add_general_parsers(parser)
+    args = parser.parse_args()
+    return args
+
+
+if __name__ == '__main__':
+    args = parse_args()
+    model = LightningModel(
+        torch_dtype=torch.float32 if args.precision == "32" else torch.float16,
+        pretrained_weights=[args.pretrained_path],
+        learning_rate=args.learning_rate,
+        use_gradient_checkpointing=args.use_gradient_checkpointing,
+        lora_rank=args.lora_rank,
+        lora_alpha=args.lora_alpha,
+        lora_target_modules=args.lora_target_modules
+    )
+    launch_training_task(model, args)