[LoRA] make lora alpha and dropout configurable

examples/advanced_diffusion_training/train_dreambooth_lora_flux_advanced.py

@@ -430,6 +430,9 @@ def parse_args(input_args=None):
         default=4,
         help=("The dimension of the LoRA update matrices."),
     )
+
+    parser.add_argument("--lora_dropout", type=float, default=0.0, help="Dropout probability for LoRA layers")
+
     parser.add_argument(
         "--with_prior_preservation",
         default=False,
@@ -1554,6 +1557,7 @@ def main(args):
     transformer_lora_config = LoraConfig(
         r=args.rank,
         lora_alpha=args.rank,
+        lora_dropout=args.lora_dropout,
         init_lora_weights="gaussian",
         target_modules=target_modules,
     )
@@ -1562,6 +1566,7 @@ def main(args):
         text_lora_config = LoraConfig(
             r=args.rank,
             lora_alpha=args.rank,
+            lora_dropout=args.lora_dropout,
             init_lora_weights="gaussian",
             target_modules=["q_proj", "k_proj", "v_proj", "out_proj"],
         )

examples/advanced_diffusion_training/train_dreambooth_lora_sd15_advanced.py

@@ -658,6 +658,8 @@ def parse_args(input_args=None):
         default=4,
         help=("The dimension of the LoRA update matrices."),
     )
+    parser.add_argument("--lora_dropout", type=float, default=0.0, help="Dropout probability for LoRA layers")
+
     parser.add_argument(
         "--use_dora",
         action="store_true",
@@ -1248,6 +1250,7 @@ def main(args):
     unet_lora_config = LoraConfig(
         r=args.rank,
         lora_alpha=args.rank,
+        lora_dropout=args.lora_dropout,
         use_dora=args.use_dora,
         init_lora_weights="gaussian",
         target_modules=["to_k", "to_q", "to_v", "to_out.0"],
@@ -1260,6 +1263,7 @@ def main(args):
         text_lora_config = LoraConfig(
             r=args.rank,
             lora_alpha=args.rank,
+            lora_dropout=args.lora_dropout,
             use_dora=args.use_dora,
             init_lora_weights="gaussian",
             target_modules=["q_proj", "k_proj", "v_proj", "out_proj"],

examples/advanced_diffusion_training/train_dreambooth_lora_sdxl_advanced.py

@@ -767,6 +767,9 @@ def parse_args(input_args=None):
         default=4,
         help=("The dimension of the LoRA update matrices."),
     )
+
+    parser.add_argument("--lora_dropout", type=float, default=0.0, help="Dropout probability for LoRA layers")
+
     parser.add_argument(
         "--use_dora",
         action="store_true",
@@ -1558,6 +1561,7 @@ def main(args):
         r=args.rank,
         use_dora=args.use_dora,
         lora_alpha=args.rank,
+        lora_dropout=args.lora_dropout,
         init_lora_weights="gaussian",
         target_modules=target_modules,
     )
@@ -1570,6 +1574,7 @@ def main(args):
             r=args.rank,
             use_dora=args.use_dora,
             lora_alpha=args.rank,
+            lora_dropout=args.lora_dropout,
             init_lora_weights="gaussian",
             target_modules=["q_proj", "k_proj", "v_proj", "out_proj"],
         )

examples/dreambooth/train_dreambooth_lora.py

@@ -524,6 +524,9 @@ def parse_args(input_args=None):
         default=4,
         help=("The dimension of the LoRA update matrices."),
     )
+
+    parser.add_argument("--lora_dropout", type=float, default=0.0, help="Dropout probability for LoRA layers")
+
     parser.add_argument(
         "--image_interpolation_mode",
         type=str,
@@ -932,6 +935,7 @@ def main(args):
     unet_lora_config = LoraConfig(
         r=args.rank,
         lora_alpha=args.rank,
+        lora_dropout=args.lora_dropout,
         init_lora_weights="gaussian",
         target_modules=["to_k", "to_q", "to_v", "to_out.0", "add_k_proj", "add_v_proj"],
     )
@@ -942,6 +946,7 @@ def main(args):
         text_lora_config = LoraConfig(
             r=args.rank,
             lora_alpha=args.rank,
+            lora_dropout=args.lora_dropout,
             init_lora_weights="gaussian",
             target_modules=["q_proj", "k_proj", "v_proj", "out_proj"],
         )

examples/dreambooth/train_dreambooth_lora_flux.py

@@ -358,6 +358,9 @@ def parse_args(input_args=None):
         default=4,
         help=("The dimension of the LoRA update matrices."),
     )
+
+    parser.add_argument("--lora_dropout", type=float, default=0.0, help="Dropout probability for LoRA layers")
+
     parser.add_argument(
         "--with_prior_preservation",
         default=False,
@@ -1236,6 +1239,7 @@ def main(args):
     transformer_lora_config = LoraConfig(
         r=args.rank,
         lora_alpha=args.rank,
+        lora_dropout=args.lora_dropout,
         init_lora_weights="gaussian",
         target_modules=target_modules,
     )
@@ -1244,6 +1248,7 @@ def main(args):
         text_lora_config = LoraConfig(
             r=args.rank,
             lora_alpha=args.rank,
+            lora_dropout=args.lora_dropout,
             init_lora_weights="gaussian",
             target_modules=["q_proj", "k_proj", "v_proj", "out_proj"],
         )

examples/dreambooth/train_dreambooth_lora_hidream.py

@@ -417,6 +417,9 @@ def parse_args(input_args=None):
         default=4,
         help=("The dimension of the LoRA update matrices."),
     )
+
+    parser.add_argument("--lora_dropout", type=float, default=0.0, help="Dropout probability for LoRA layers")
+
     parser.add_argument(
         "--with_prior_preservation",
         default=False,
@@ -1161,6 +1164,7 @@ def main(args):
     transformer_lora_config = LoraConfig(
         r=args.rank,
         lora_alpha=args.rank,
+        lora_dropout=args.lora_dropout,
         init_lora_weights="gaussian",
         target_modules=target_modules,
     )

examples/dreambooth/train_dreambooth_lora_lumina2.py

@@ -328,6 +328,9 @@ def parse_args(input_args=None):
         default=4,
         help=("The dimension of the LoRA update matrices."),
     )
+
+    parser.add_argument("--lora_dropout", type=float, default=0.0, help="Dropout probability for LoRA layers")
+
     parser.add_argument(
         "--with_prior_preservation",
         default=False,
@@ -1023,6 +1026,7 @@ def main(args):
     transformer_lora_config = LoraConfig(
         r=args.rank,
         lora_alpha=args.rank,
+        lora_dropout=args.lora_dropout,
         init_lora_weights="gaussian",
         target_modules=target_modules,
     )

examples/dreambooth/train_dreambooth_lora_sana.py

@@ -323,6 +323,9 @@ def parse_args(input_args=None):
         default=4,
         help=("The dimension of the LoRA update matrices."),
     )
+
+    parser.add_argument("--lora_dropout", type=float, default=0.0, help="Dropout probability for LoRA layers")
+
     parser.add_argument(
         "--with_prior_preservation",
         default=False,
@@ -1021,6 +1024,7 @@ def main(args):
     transformer_lora_config = LoraConfig(
         r=args.rank,
         lora_alpha=args.rank,
+        lora_dropout=args.lora_dropout,
         init_lora_weights="gaussian",
         target_modules=target_modules,
     )

examples/dreambooth/train_dreambooth_lora_sd3.py

@@ -367,6 +367,9 @@ def parse_args(input_args=None):
         default=4,
         help=("The dimension of the LoRA update matrices."),
     )
+
+    parser.add_argument("--lora_dropout", type=float, default=0.0, help="Dropout probability for LoRA layers")
+
     parser.add_argument(
         "--with_prior_preservation",
         default=False,
@@ -1264,6 +1267,7 @@ def main(args):
     transformer_lora_config = LoraConfig(
         r=args.rank,
         lora_alpha=args.rank,
+        lora_dropout=args.lora_dropout,
         init_lora_weights="gaussian",
         target_modules=target_modules,
     )
@@ -1273,6 +1277,7 @@ def main(args):
         text_lora_config = LoraConfig(
             r=args.rank,
             lora_alpha=args.rank,
+            lora_dropout=args.lora_dropout,
             init_lora_weights="gaussian",
             target_modules=["q_proj", "k_proj", "v_proj", "out_proj"],
         )

examples/dreambooth/train_dreambooth_lora_sdxl.py

@@ -659,6 +659,9 @@ def parse_args(input_args=None):
         default=4,
         help=("The dimension of the LoRA update matrices."),
     )
+
+    parser.add_argument("--lora_dropout", type=float, default=0.0, help="Dropout probability for LoRA layers")
+
     parser.add_argument(
         "--use_dora",
         action="store_true",
@@ -1199,10 +1202,11 @@ def main(args):
             text_encoder_one.gradient_checkpointing_enable()
             text_encoder_two.gradient_checkpointing_enable()
 
-    def get_lora_config(rank, use_dora, target_modules):
+    def get_lora_config(rank, dropout, use_dora, target_modules):
         base_config = {
             "r": rank,
             "lora_alpha": rank,
+            "lora_dropout": dropout,
             "init_lora_weights": "gaussian",
             "target_modules": target_modules,
         }
@@ -1218,14 +1222,24 @@ def get_lora_config(rank, use_dora, target_modules):
 
     # now we will add new LoRA weights to the attention layers
     unet_target_modules = ["to_k", "to_q", "to_v", "to_out.0"]
-    unet_lora_config = get_lora_config(rank=args.rank, use_dora=args.use_dora, target_modules=unet_target_modules)
+    unet_lora_config = get_lora_config(
+        rank=args.rank,
+        dropout=args.lora_dropout,
+        use_dora=args.use_dora,
+        target_modules=unet_target_modules,
+    )
     unet.add_adapter(unet_lora_config)
 
     # The text encoder comes from 🤗 transformers, so we cannot directly modify it.
     # So, instead, we monkey-patch the forward calls of its attention-blocks.
     if args.train_text_encoder:
         text_target_modules = ["q_proj", "k_proj", "v_proj", "out_proj"]
-        text_lora_config = get_lora_config(rank=args.rank, use_dora=args.use_dora, target_modules=text_target_modules)
+        text_lora_config = get_lora_config(
+            rank=args.rank,
+            dropout=args.lora_dropout,
+            use_dora=args.use_dora,
+            target_modules=text_target_modules,
+        )
         text_encoder_one.add_adapter(text_lora_config)
         text_encoder_two.add_adapter(text_lora_config)